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The following lists the recent preprints posted on EGUsphere with GMD-related topics, the recent preprints posted in GMD’s discussion forum, as well as final revised papers published recently in GMD.
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07 Mar 2024
Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)
Michaja Pehl, Felix Schreyer, and Gunnar Luderer
Geosci. Model Dev., 17, 2015–2038, https://doi.org/10.5194/gmd-17-2015-2024,https://doi.org/10.5194/gmd-17-2015-2024, 2024
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We extend the REMIND model (used to investigate climate mitigation strategies) by an industry module that represents cement, chemical, steel, and other industries. We also present a method for deriving scenarios of industry subsector activity and energy demand, consistent with established socioeconomic scenarios, allowing us to investigate the different climate change mitigation challenges and strategies in industry subsectors in the context of the entire energy–economy–climate system.
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07 Mar 2024
Inverse modelling for surface methane flux estimation with 4DVar: impact of a computationally efficient representation of a non-diagonal B-matrix in INVICAT v4
Ross Noel Bannister and Chris Wilson
EGUsphere, https://doi.org/10.5194/egusphere-2024-655,https://doi.org/10.5194/egusphere-2024-655, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Prior information is essential for the top-down estimation of CH4 surface fluxes. Errors in the prior are correlated in time/space, but accounting for correlations can be costly. We report on an efficient scheme to represent correlations in the inverse modelling system, INVICAT. The method is tested by assimilating CH4 observations using the scheme. Our findings show that accounting for spatio-temporal correlations improve CH4 flux estimates, demonstrating that the method should be further used.
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07 Mar 2024
CICERO Simple Climate Model (CICERO-SCM v1.1.1) – an improved simple climate model with a parameter calibration tool
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Peters, Bjørn Hallvard Samset, Benjamin M. Sanderson, and Ragnhild Bieltvedt Skeie
EGUsphere, https://doi.org/10.5194/egusphere-2024-196,https://doi.org/10.5194/egusphere-2024-196, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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The CICERO-SCM has existed as a FORTRAN model since 1999 and consists of a part that calculates radiative forcing and concentrations from emissions, and an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper we describe an updated version ported to python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
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06 Mar 2024
The Global Forest Fire Emissions Prediction System version 1.0
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul Makar, and Dan Thompson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-31,https://doi.org/10.5194/gmd-2024-31, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
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06 Mar 2024
Development And Application of WRF(v4.1.2)-uEMEP(v5) Model at the City with the Highest Industrial Density: A Case Study of Foshan
Liting Yang, Ming Chang, Shuping Situ, Weiwen Wang, and Xuemei Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-28,https://doi.org/10.5194/egusphere-2024-28, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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The study aims to develop and apply the WRF-uEMEP model to simulate air quality at the city scale, with a focus on Foshan, the city with the highest industrial density. The research process included model development, calibration, and validation using existing air quality data in Foshan. Research shows that WRF-uEMEP model effectively captures the impact of urban structure on air pollutant processes and reveals the spatial and temporal distribution of air pollutants in Foshan.
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06 Mar 2024
Evaluating the meteorological transport model ensemble for accounting uncertainties in carbon flux estimation over India
Thara Anna Mathew, Aparnna Ravi, Dhanyalekshmi Pillai, Lekshmi Saradambal, Jithin S. Kumar, Manoj M. Gopalakrishnan, and Vishnu Thilakan
EGUsphere, https://doi.org/10.5194/egusphere-2023-2334,https://doi.org/10.5194/egusphere-2023-2334, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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We evaluated transport model meteorology by comparing different simulations with surface and vertical profile observations at two stations, Cochin and Gadanki, and with global reanalysis data over India for May, 2017. The errors transferred into the CO2 mixing ratio enhancement simulations. Hence, it is a significant step to characterize errors in atmospheric transport simulations as it leads to overall improvement in geo-spatial distributions of GHG sources and sinks at the regional levels.
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05 Mar 2024
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024,https://doi.org/10.5194/gmd-17-1995-2024, 2024
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Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
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05 Mar 2024
Three-dimensional geological modelling of igneous intrusions in LoopStructural v1.5.10
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024,https://doi.org/10.5194/gmd-17-1975-2024, 2024
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Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
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04 Mar 2024
Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modeling using VolcanicAshInversion v1.2.1, within the operational eEMEP (emergency European Monitoring and Evaluation Programme) volcanic plume forecasting system (version rv4_17)
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024,https://doi.org/10.5194/gmd-17-1957-2024, 2024
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It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
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04 Mar 2024
Representing the effects of giant aerosol in droplet nucleation in E3SMv2
Yu Yao, Po-Lun Ma, Yi Qin, Matthew W. Christensen, Hui Wan, Kai Zhang, Balwinder Singh, Meng Huang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-523,https://doi.org/10.5194/egusphere-2024-523, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Giant aerosols have substantial effects on warm rain formation. However, it remains challenging to quantify the impact of giant particles at global scale. In this work, we applied earth system model to investigate its impacts by implementing new giant aerosol treatments to consider its physical process. We found this approach substantially affect liquid cloud and improved model's precipitation response to aerosols. Our findings demonstrate the significant impact of giant aerosols on climate.
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04 Mar 2024
Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-407,https://doi.org/10.5194/egusphere-2024-407, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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The soil water potential (SWP) determines various soil water processes. Because it cannot be measured directly by remote sensing techniques, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of SWP.
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04 Mar 2024
A Global land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: Formulation and evaluation at instrumented sites
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova
EGUsphere, https://doi.org/10.5194/egusphere-2024-506,https://doi.org/10.5194/egusphere-2024-506, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in-situ observations are available. We find that, when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.
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01 Mar 2024
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024,https://doi.org/10.5194/gmd-17-1931-2024, 2024
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Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
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01 Mar 2024
A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers
Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont
Geosci. Model Dev., 17, 1903–1929, https://doi.org/10.5194/gmd-17-1903-2024,https://doi.org/10.5194/gmd-17-1903-2024, 2024
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In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
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01 Mar 2024
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024,https://doi.org/10.5194/gmd-17-1885-2024, 2024
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We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
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29 Feb 2024
New insights into the South China Sea throughflow and water budget seasonal cycle: evaluation and analysis of a high-resolution configuration of the ocean model SYMPHONIE version 2.4
Ngoc B. Trinh, Marine Herrmann, Caroline Ulses, Patrick Marsaleix, Thomas Duhaut, Thai To Duy, Claude Estournel, and R. Kipp Shearman
Geosci. Model Dev., 17, 1831–1867, https://doi.org/10.5194/gmd-17-1831-2024,https://doi.org/10.5194/gmd-17-1831-2024, 2024
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A high-resolution model was built to study the South China Sea (SCS) water, heat, and salt budgets. Model performance is demonstrated by comparison with observations and simulations. Important discards are observed if calculating offline, instead of online, lateral inflows and outflows of water, heat, and salt. The SCS mainly receives water from the Luzon Strait and releases it through the Mindoro, Taiwan, and Karimata straits. SCS surface interocean water exchanges are driven by monsoon winds.
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29 Feb 2024
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024,https://doi.org/10.5194/gmd-17-1813-2024, 2024
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The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
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29 Feb 2024
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024,https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
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29 Feb 2024
Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024,https://doi.org/10.5194/gmd-17-1789-2024, 2024
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Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
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28 Feb 2024
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024,https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
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28 Feb 2024
MQGeometry-1.0: a multi-layer quasi-geostrophic solver on non-rectangular geometries
Louis Thiry, Long Li, Guillaume Roullet, and Etienne Mémin
Geosci. Model Dev., 17, 1749–1764, https://doi.org/10.5194/gmd-17-1749-2024,https://doi.org/10.5194/gmd-17-1749-2024, 2024
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We present a new way of solving the quasi-geostrophic (QG) equations, a simple set of equations describing ocean dynamics. Our method is solely based on the numerical methods used to solve the equations and requires no parameter tuning. Moreover, it can handle non-rectangular geometries, opening the way to study QG equations on realistic domains. We release a PyTorch implementation to ease future machine-learning developments on top of the presented method.
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27 Feb 2024
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024,https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
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27 Feb 2024
A nonhydrostatic formulation for MPAS-Ocean
Sara Calandrini, Darren Engwirda, and Luke Van Roekel
EGUsphere, https://doi.org/10.5194/egusphere-2024-472,https://doi.org/10.5194/egusphere-2024-472, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Most modern ocean circulation models only consider the hydrostatic pressure, but for coastal phenomena nonhydrostatic effects become important, creating the need to include the nonhydrostatic pressure. In this work, we present a nonhydrostatic formulation for MPAS-Ocean (MPAS-O NH) and show its correctness on idealized benchmark test cases. MPAS-O NH is the first global nonhydrostatic model at variable resolution and is the first nonhydrostatic ocean model to be fully coupled in a climate model.
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27 Feb 2024
An Updated Parameterization of the Unstable Atmospheric Surface Layer in WRF Modeling System
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-3,https://doi.org/10.5194/gmd-2024-3, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Inadequate representation of surface-atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve WRF model version 4.2.2 by introducing a unique theoretical framework suggested by Kader and Yaglom (1990) under convective conditions. In addition, to enhance the potential applicability of WRF modeling system, various commonly used similarity functions under convective conditions have also been incorporated.
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26 Feb 2024
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024,https://doi.org/10.5194/gmd-17-1667-2024, 2024
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We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
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26 Feb 2024
Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Geosci. Model Dev., 17, 1651–1665, https://doi.org/10.5194/gmd-17-1651-2024,https://doi.org/10.5194/gmd-17-1651-2024, 2024
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Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
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26 Feb 2024
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024,https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
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26 Feb 2024
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024,https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
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26 Feb 2024
A revised ocean mixed layer model for better simulating the diurnal variation of ocean skin temperature
Eui-Jong Kang, Byung-Ju Sohn, Sang-Woo Kim, Wonho Kim, Young-Cheol Kwon, Seung-Bum Kim, Hyoung-Wook Chun, and Chao Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-23,https://doi.org/10.5194/gmd-2024-23, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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The recently available ERA5 hourly ocean skin temperature (Tint) data is expected to be valuable for various science studies. However, when analyzing the hourly variations of Tint, questions arise about its reliability, the deficiency of which may be related to errors in the ocean mixed layer (OML) model. To address this, we reexamined and corrected significant errors in the OML model. Validation of the simulated SST using the revised OML model against observations demonstrated good agreement.
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26 Feb 2024
A rapid application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
Camilla Therese Mathison, Eleanor Burke, Eszter Kovacs, Gregory Munday, Chris Huntingford, Chris Jones, Chris Smith, Norman Steinert, Andy Wiltshire, Laila Gohar, and Rebecca Varney
EGUsphere, https://doi.org/10.5194/egusphere-2023-2932,https://doi.org/10.5194/egusphere-2023-2932, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emission scenarios and rapidly provide regional impacts information. PRIME allows large ensembles to be run on multi-centennial timescales including analysis of many important variables for impacts assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios giving confidence in using PRIME for novel scenarios.
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23 Feb 2024
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024,https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
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23 Feb 2024
An automatic mesh generator for coupled 1D–2D hydrodynamic models
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024,https://doi.org/10.5194/gmd-17-1603-2024, 2024
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Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
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23 Feb 2024
Multivariate adjustment of drizzle bias using machine learning in European climate projections
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
EGUsphere, https://doi.org/10.5194/egusphere-2024-45,https://doi.org/10.5194/egusphere-2024-45, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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This study focused on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies were applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method of Random Forest for increasing the accuracy of climate models, concerning the projection of the number of wet days.
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23 Feb 2024
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
EGUsphere, https://doi.org/10.5194/egusphere-2024-235,https://doi.org/10.5194/egusphere-2024-235, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Marine Cloud Brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed; However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume spreading model.
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22 Feb 2024
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024,https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
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22 Feb 2024
Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024,https://doi.org/10.5194/gmd-17-1563-2024, 2024
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A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
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22 Feb 2024
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024,https://doi.org/10.5194/gmd-17-1543-2024, 2024
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The tangent linear and adjoint models have been successfully implemented in the MPAS-CO2 system, which has undergone rigorous accuracy testing. This development lays the groundwork for a global carbon flux data assimilation system, which offers the flexibility of high-resolution focus on specific areas, while maintaining a coarser resolution elsewhere. This approach significantly reduces computational costs and is thus perfectly suited for future CO2 geostationery and imager satellites.
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22 Feb 2024
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, and Christoph Müller
EGUsphere, https://doi.org/10.5194/egusphere-2023-2946,https://doi.org/10.5194/egusphere-2023-2946, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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We present a new approach to model biological nitrogen fixation (BNF) in the Lund Potsdam Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, the nitrogen (N) deficit and carbon (C) costs. The new approach improved global sums and spatial patterns of BNF compared to the scientific literature and the models’ ability to project future C and N cycle dynamics.
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22 Feb 2024
Generalized drought index: A novel multi-scale daily approach for drought assessment
João Careto, Rita Cardoso, Ana Russo, Daniela Lima, and Pedro Soares
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-9,https://doi.org/10.5194/gmd-2024-9, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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In this study, a new drought index is proposed, which not only is able to identify the same events but also can improve the results obtained from other established drought indices. The index is empirically based and is extremely straightforward to compute. It is as well, a daily drought index with the ability to not only assess flash droughts but also events at longer aggregation scales, such as the traditional monthly indices.
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22 Feb 2024
Towards a real-time modeling of global ocean waves by the fully GPU-accelerated spectral wave model WAM6-GPU
Ye Yuan, Fujiang Yu, Zhi Chen, Xueding Li, Fang Hou, Yuanyong Gao, Zhiyi Gao, and Renbo Pang
EGUsphere, https://doi.org/10.5194/egusphere-2024-169,https://doi.org/10.5194/egusphere-2024-169, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Accurate and timely forecasting of ocean waves is of great importance to the safety of marine transportation and offshore engineering. In this study, Graphics Processing Unit (GPU)-accelerated computing is introduced in the WAM (Cycle 6). With this effort, global high-resolution wave simulations now can run on GPUs up to tens of times faster than currently available models on a CPU node with just as accurate results.
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21 Feb 2024
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024,https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
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21 Feb 2024
Southern Ocean Ice Prediction System version 1.0 (SOIPS v1.0): description of the system and evaluation of synoptic-scale sea ice forecasts
Fu Zhao, Xi Liang, Zhongxiang Tian, Ming Li, Na Liu, and Chengyan Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-4,https://doi.org/10.5194/gmd-2024-4, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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In this work, we introduce a newly developed Antarctic sea ice forecasting system, namely Southern Ocean Ice Prediction System (SOIPS). The system is based on a regional sea-ice‒ocean‒ice-shelf coupled model and can assimilate sea ice concentration observations. Through assessing the system performance on sea ice forecasts, we find that the system can provide reliable Antarctic sea ice forecasts for next 7 days, and has the potential to guide ship navigation in the Antarctic sea ice zone.
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20 Feb 2024
Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024,https://doi.org/10.5194/gmd-17-1511-2024, 2024
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Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
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20 Feb 2024
Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024,https://doi.org/10.5194/gmd-17-1497-2024, 2024
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Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
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20 Feb 2024
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
Sam Oliver Owens, Dipanjan Majumdar, Christopher Edward Wilson, Paul Bartholomew, and Maarten van Reeuwijk
EGUsphere, https://doi.org/10.5194/egusphere-2024-96,https://doi.org/10.5194/egusphere-2024-96, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical framework uDALES, which can model microscale airflow, heat transfer and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately, and to take advantage of the resources available on current and future high-performance computing systems.
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20 Feb 2024
HyPhAI v1.0: Hybrid Physics-AI architecture for cloud cover nowcasting
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
EGUsphere, https://doi.org/10.5194/egusphere-2023-3078,https://doi.org/10.5194/egusphere-2023-3078, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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This study introduces a novel approach, combining Physics and Artificial Intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional Deep Learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods, and contributes to open up new possibilities for combining physical knowledge with deep learning models.
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19 Feb 2024
Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024,https://doi.org/10.5194/gmd-17-1469-2024, 2024
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Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
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16 Feb 2024
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024,https://doi.org/10.5194/gmd-17-1387-2024, 2024
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Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
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16 Feb 2024
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024,https://doi.org/10.5194/gmd-17-1409-2024, 2024
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Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
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16 Feb 2024
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024,https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
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16 Feb 2024
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024,https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
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16 Feb 2024
The CHIMERE chemistry-transport model v2023r1
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-20,https://doi.org/10.5194/gmd-2024-20, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same set-up. Processes have been modified and updated such as turbulence, transport schemes and dry deposition.
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16 Feb 2024
A new lightning scheme in Canada's Atmospheric Model, CanAM5.1: Implementation, evaluation, and projections of lightning and fire in future climates
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-24,https://doi.org/10.5194/gmd-2024-24, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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This paper describes how lightning was added as a process in the Canadian Earth System model, in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to changing climate.
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16 Feb 2024
Physics-motivated Cell-octree Adaptive Mesh Refinement in the Vlasiator 5.3 Global Hybrid-Vlasov Code
Leo Kotipalo, Markus Battarbee, Yann Pfau-Kempf, and Minna Palmroth
EGUsphere, https://doi.org/10.5194/egusphere-2024-301,https://doi.org/10.5194/egusphere-2024-301, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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This paper examines a method called adaptive mesh refinement in optimization of the space plasma simulation model Vlasiator. The method locally adjusts resolution in regions which are most relevant to model, based on the properties of the plasma. The runs testing this method show that adaptive refinement manages to highlight the desired regions with manageable performance overhead. Performance in larger scale production runs and mitigation of overhead are avenues of further research.
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15 Feb 2024
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024,https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
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15 Feb 2024
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024,https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
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14 Feb 2024
SnowPappus v1.0, a blowing-snow model for large-scale applications of the Crocus snow scheme
Matthieu Baron, Ange Haddjeri, Matthieu Lafaysse, Louis Le Toumelin, Vincent Vionnet, and Mathieu Fructus
Geosci. Model Dev., 17, 1297–1326, https://doi.org/10.5194/gmd-17-1297-2024,https://doi.org/10.5194/gmd-17-1297-2024, 2024
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Increasing the spatial resolution of numerical systems simulating snowpack evolution in mountain areas requires representing small-scale processes such as wind-induced snow transport. We present SnowPappus, a simple scheme coupled with the Crocus snow model to compute blowing-snow fluxes and redistribute snow among grid points at 250 m resolution. In terms of numerical cost, it is suitable for large-scale applications. We present point-scale evaluations of fluxes and snow transport occurrence.
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14 Feb 2024
jsmetrics v0.2.0: a Python package for metrics and algorithms used to identify or characterise atmospheric jet streams
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024,https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
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14 Feb 2024
| Review and perspective paper
Towards the definition of a solar forcing dataset for CMIP7
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024,https://doi.org/10.5194/gmd-17-1217-2024, 2024
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Executive editor
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
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Executive editor
This manuscript is a timely publication necessary for preparations for CMIP7, given the importance of solar forcing to the climate system. It encourages community discussion, which will certainly be ongoing for the next several years.
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14 Feb 2024
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024,https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
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14 Feb 2024
The implementation of dust mineralogy in COSMO5.05-MUSCAT
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024,https://doi.org/10.5194/gmd-17-1271-2024, 2024
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Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
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13 Feb 2024
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024,https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
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13 Feb 2024
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024,https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
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13 Feb 2024
OpenFOAM-avalanche 2312: Depth-integrated Models Beyond Dense Flow Avalanches
Matthias Rauter and Julia Kowalski
EGUsphere, https://doi.org/10.5194/egusphere-2024-210,https://doi.org/10.5194/egusphere-2024-210, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Snow avalanches can form large powder clouds that substantially exceed the velocity and reach of the dense core. Only a few and complex models exist to simulate this phenomenon, and the respective hazard is hard to predict. This work provides a novel flow model that focuses on simple relations while still encapsulating the significant behaviour. The model is applied to reconstruct two catastrophic powder snow avalanche events in Austria.
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12 Feb 2024
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024,https://doi.org/10.5194/gmd-17-1133-2024, 2024
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The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
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12 Feb 2024
GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024,https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
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12 Feb 2024
Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024,https://doi.org/10.5194/gmd-17-1111-2024, 2024
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This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
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12 Feb 2024
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
EGUsphere, https://doi.org/10.5194/egusphere-2023-2839,https://doi.org/10.5194/egusphere-2023-2839, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Atmospheric model users oftentimes overlook the impact of the land-atmosphere interaction. This study accessed various different setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization, allows for certain improvement by constraining the soil moisture fields, and, through their impact on atmospheric mixing, to improve atmospheric transport.
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12 Feb 2024
Exploring the Potential of History Matching for Land Surface Model Calibration
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2996,https://doi.org/10.5194/egusphere-2023-2996, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. Here we test a technique called “History matching” against more common approaches. This method adapts well to these models, helping better understand how they work and therefore how to make them more realistic.
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12 Feb 2024
The Paleochrono-1.1 probabilistic model to derive optimized and consistent chronologies for several paleoclimatic sites
Frédéric Parrenin, Marie Bouchet, Christo Buizert, Emilie Capron, Ellen Corrick, Russell Drysdale, Kenji Kawamura, Amaëlle Landais, Robert Mulvaney, Ikumi Oyabu, and Sune Rasmussen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2911,https://doi.org/10.5194/egusphere-2023-2911, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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The Paleochrono1 probablistic dating model allows to derive a common and optimized chronology for several paleoclimatic sites from various archives (ice cores, speleothems, marine cores, lake cores, etc.). It combines prior sedimentation scenarios with chronological information such as dated horizons, dated intervals, stratigraphic links and (for ice cores) Delta-depth observations. Paleochrono1 is available under the MIT open-source license.
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09 Feb 2024
Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024,https://doi.org/10.5194/gmd-17-1091-2024, 2024
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With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
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09 Feb 2024
Observational operator for fair model calibration with ground NO2 measurements
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-216,https://doi.org/10.5194/gmd-2023-216, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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The model evaluation against ground observations is usually unfair. The former simulates mean status over coarse grids while the latter represents the very surrounding atmosphere. To solve this, we proposed a new approach called "LUBR" that considers the intra-grid variance. The LUBR is validated to provide insights that align with satellite OMI measurements. The results highlight the importance of considering fine-scale urban-rural differences when comparing models and observation.
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08 Feb 2024
Constraining the carbon cycle in JULES-ES-1.0
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024,https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
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08 Feb 2024
A stochastic parameterization of ice sheet surface mass balance for the Stochastic Ice-Sheet and Sea-Level System Model (StISSM v1.0)
Lizz Ultee, Alexander A. Robel, and Stefano Castruccio
Geosci. Model Dev., 17, 1041–1057, https://doi.org/10.5194/gmd-17-1041-2024,https://doi.org/10.5194/gmd-17-1041-2024, 2024
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The surface mass balance (SMB) of an ice sheet describes the net gain or loss of mass from ice sheets (such as those in Greenland and Antarctica) through interaction with the atmosphere. We developed a statistical method to generate a wide range of SMB fields that reflect the best understanding of SMB processes. Efficiently sampling the variability of SMB will help us understand sources of uncertainty in ice sheet model projections.
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07 Feb 2024
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024,https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
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07 Feb 2024
Great Lakes wave forecast system on high-resolution unstructured meshes
Ali Abdolali, Saeideh Banihashemi, Jose Henrique Alves, Aron Roland, Tyler J. Hesser, Mary Anderson Bryant, and Jane McKee Smith
Geosci. Model Dev., 17, 1023–1039, https://doi.org/10.5194/gmd-17-1023-2024,https://doi.org/10.5194/gmd-17-1023-2024, 2024
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This article presents an overview of the development and implementation of Great Lake Wave Unstructured (GLWUv2.0), including the core model and workflow design and development. The validation was conducted against in situ data for the re-forecasted duration for summer and wintertime (ice season). The article describes the limitations and challenges encountered in the operational environment and the path forward for the next generation of wave forecast systems in enclosed basins like the GL.
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07 Feb 2024
Impact of ITCZ width on global climate: ITCZ-MIP
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-17,https://doi.org/10.5194/gmd-2024-17, 2024
Preprint under review for GMD (discussion: open, 2 comments)
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The width of the tropical rain belt affects many aspects of our climate; yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes; but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
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06 Feb 2024
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024,https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
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Geoscientific Model Development
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Executive editors: David Ham, Juan Antonio Añel, Astrid Kerkweg, Min-Hui Lo, Richard Neale, Rolf Sander & Paul Ullrich
eISSN: GMD 1991-9603, GMDD 1991-962X
Geoscientific Model Development (GMD) is a not-for-profit international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication:
geoscientific model descriptions, from statistical models to box models to GCMs;
development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results;
new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data;
papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data;
model experiment descriptions, including experimental details and project protocols;
full evaluations of previously published models.
More details can be found in manuscript types and the journal editorial (compiled by the executive editors).
"I believe that the time is ripe for significantly better documentation of programs, and that we can best achieve this by considering programs to be works of literature." (Donald E. Knuth, Literate Programming, 1984)
"Essentially, all models are wrong, but some are useful." (George E. P. Box, Robustness in the strategy of scientific model building, 1979)
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JIF5.1
JIF 5-year7
CiteScore9.3
Google h5-index76
Highlight articles
21 Dec 2023
| Highlight paper
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023,https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Executive editor
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
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Executive editor
This manuscript provides a novel and comprehensive assessment of uncertainty associated with sea level rise. The model description is thorough and it is applied to a number of possible scenarios. The conclusions are important for framing future discussions on sea level rise.
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16 Nov 2023
| Highlight paper
Universal differential equations for glacier ice flow modelling
Jordi Bolibar, Facundo Sapienza, Fabien Maussion, Redouane Lguensat, Bert Wouters, and Fernando Pérez
Geosci. Model Dev., 16, 6671–6687, https://doi.org/10.5194/gmd-16-6671-2023,https://doi.org/10.5194/gmd-16-6671-2023, 2023
Short summary
Executive editor
Short summary
We developed a new modelling framework combining numerical methods with machine learning. Using this approach, we focused on understanding how ice moves within glaciers, and we successfully learnt a prescribed law describing ice movement for 17 glaciers worldwide as a proof of concept. Our framework has the potential to discover important laws governing glacier processes, aiding our understanding of glacier physics and their contribution to water resources and sea-level rise.
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Executive editor
The integration of neural networks into PDE solvers to simulate systems for which the PDE models are incomplete is a key advance at the cutting edge of geoscientific modelling. The approach presented here is applicable far beyond the realm of ice modelling, and will be of interest to model developers and users across geoscience and beyond.
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14 Nov 2023
| Review and perspective paper
| Highlight paper
Machine learning for numerical weather and climate modelling: a review
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023,https://doi.org/10.5194/gmd-16-6433-2023, 2023
Short summary
Executive editor
Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
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Executive editor
Machine Learning is a rapidly expanding technique in the field of weather and climate modelling. This paper takes stock of the state of the field at the present time, and will be invaluable to participants across the field and beyond who wish to understand the impact of Machine Learning on the field, its limitations, and current scope.
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06 Oct 2023
| Highlight paper
Emulating lateral gravity wave propagation in a global chemistry–climate model (EMAC v2.55.2) through horizontal flux redistribution
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchař, Patrick Jöckel, Astrid Kerkweg, and Bastian Kern
Geosci. Model Dev., 16, 5561–5583, https://doi.org/10.5194/gmd-16-5561-2023,https://doi.org/10.5194/gmd-16-5561-2023, 2023
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Executive editor
Short summary
The columnar approach of gravity wave (GW) schemes results in dynamical model biases, but parallel decomposition makes horizontal GW propagation computationally unfeasible. In the global model EMAC, we approximate it by GW redistribution at one altitude using tailor-made redistribution maps generated with a ray tracer. More spread-out GW drag helps reconcile the model with observations and close the 60°S GW gap. Polar vortex dynamics are improved, enhancing climate model credibility.
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Executive editor
Grave wave (GW) parameterisations currently used in state-of-the-art weather and climate models are based on a purely columnar approach, which does not allow for any horizontal propagation of GWs and has been identified as potential source of systematic biases in the simulation of middle atmospheric dynamics. The study by Eichinger and colleagues presents now a computationally efficient method to emulate the effects of lateral propagation of orographic GWs in climate models by horizontal momentum flux redistribution using redistribution maps derived from a GW ray-tracing model. The presented approach is an important step towards a better representation of orographic GWs in climate models, which might improve long-standing problems in atmospheric modelling.
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02 Aug 2023
| Highlight paper
The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023,https://doi.org/10.5194/gmd-16-4427-2023, 2023
Short summary
Executive editor
Short summary
We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
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Executive editor
This paper investigates an impactful topic, is easily digestible to non-scientists, is well written, has nice visuals, uses novel objective identification methods and has well documented and accessible code.
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Recent papers
07 Mar 2024
Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)
Michaja Pehl, Felix Schreyer, and Gunnar Luderer
Geosci. Model Dev., 17, 2015–2038, https://doi.org/10.5194/gmd-17-2015-2024,https://doi.org/10.5194/gmd-17-2015-2024, 2024
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We extend the REMIND model (used to investigate climate mitigation strategies) by an industry module that represents cement, chemical, steel, and other industries. We also present a method for deriving scenarios of industry subsector activity and energy demand, consistent with established socioeconomic scenarios, allowing us to investigate the different climate change mitigation challenges and strategies in industry subsectors in the context of the entire energy–economy–climate system.
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07 Mar 2024
Inverse modelling for surface methane flux estimation with 4DVar: impact of a computationally efficient representation of a non-diagonal B-matrix in INVICAT v4
Ross Noel Bannister and Chris Wilson
EGUsphere, https://doi.org/10.5194/egusphere-2024-655,https://doi.org/10.5194/egusphere-2024-655, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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Prior information is essential for the top-down estimation of CH4 surface fluxes. Errors in the prior are correlated in time/space, but accounting for correlations can be costly. We report on an efficient scheme to represent correlations in the inverse modelling system, INVICAT. The method is tested by assimilating CH4 observations using the scheme. Our findings show that accounting for spatio-temporal correlations improve CH4 flux estimates, demonstrating that the method should be further used.
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06 Mar 2024
The Global Forest Fire Emissions Prediction System version 1.0
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul Makar, and Dan Thompson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-31,https://doi.org/10.5194/gmd-2024-31, 2024
Preprint under review for GMD (discussion: open, 0 comments)
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
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06 Mar 2024
Development And Application of WRF(v4.1.2)-uEMEP(v5) Model at the City with the Highest Industrial Density: A Case Study of Foshan
Liting Yang, Ming Chang, Shuping Situ, Weiwen Wang, and Xuemei Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-28,https://doi.org/10.5194/egusphere-2024-28, 2024
Preprint under review for GMD (discussion: open, 0 comments)
Short summary
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The study aims to develop and apply the WRF-uEMEP model to simulate air quality at the city scale, with a focus on Foshan, the city with the highest industrial density. The research process included model development, calibration, and validation using existing air quality data in Foshan. Research shows that WRF-uEMEP model effectively captures the impact of urban structure on air pollutant processes and reveals the spatial and temporal distribution of air pollutants in Foshan.
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06 Mar 2024
Evaluating the meteorological transport model ensemble for accounting uncertainties in carbon flux estimation over India
Thara Anna Mathew, Aparnna Ravi, Dhanyalekshmi Pillai, Lekshmi Saradambal, Jithin S. Kumar, Manoj M. Gopalakrishnan, and Vishnu Thilakan
EGUsphere, https://doi.org/10.5194/egusphere-2023-2334,https://doi.org/10.5194/egusphere-2023-2334, 2024
Preprint under review for GMD (discussion: open, 0 comments)
Short summary
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We evaluated transport model meteorology by comparing different simulations with surface and vertical profile observations at two stations, Cochin and Gadanki, and with global reanalysis data over India for May, 2017. The errors transferred into the CO2 mixing ratio enhancement simulations. Hence, it is a significant step to characterize errors in atmospheric transport simulations as it leads to overall improvement in geo-spatial distributions of GHG sources and sinks at the regional levels.
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News
06 Feb 2024
Statement on the use of AI-based tools in publications
Tools based on artificial intelligence (AI) are increasingly being used to create scientific documents, including peer-reviewed publications, preprints and conference contributions. Please read EGU's statement on the use of such tools in publications.
06 Feb 2024
Statement on the use of AI-based tools in publications
Tools based on artificial intelligence (AI) are increasingly being used to create scientific documents, including peer-reviewed publications, preprints and conference contributions. Please read EGU's statement on the use of such tools in publications.
17 Jan 2024
Copernicus Publications launches ROR integration for corresponding authors
Copernicus Publications started using the Research Organization Registry (ROR) database as the framework to assign institutional identifiers to corresponding authors in order to disambiguate affiliations listed on a published article and greatly enhancing the reporting capabilities to all academic stakeholders. Please read more.
17 Jan 2024
Copernicus Publications launches ROR integration for corresponding authors
Copernicus Publications started using the Research Organization Registry (ROR) database as the framework to assign institutional identifiers to corresponding authors in order to disambiguate affiliations listed on a published article and greatly enhancing the reporting capabilities to all academic stakeholders. Please read more.
15 Jan 2024
A huge thank you to our referees in 2023!
We would like to say a huge thank you to all referees for their volunteer efforts to provide fair, thorough, and constructive peer-review reports. Their invaluable contribution maintains our high scientific standards and ensures the ongoing success of our interactive open-access journals.
15 Jan 2024
A huge thank you to our referees in 2023!
We would like to say a huge thank you to all referees for their volunteer efforts to provide fair, thorough, and constructive peer-review reports. Their invaluable contribution maintains our high scientific standards and ensures the ongoing success of our interactive open-access journals.
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Notice on the current situation in Ukraine
To show our support for Ukraine, all fees for papers from authors (first or corresponding authors) affiliated to Ukrainian institutions are automatically waived, regardless if these papers are co-authored by scientists affiliated to Russian and/or Belarusian institutions. The only exception will be if the corresponding author or first contact (contractual partner of Copernicus) are from a Russian and/or Belarusian institution, in that case the APCs are not waived.
In accordance with current European restrictions, Copernicus Publications does not step into business relations with and issue APC-invoices (articles processing charges) to Russian and Belarusian institutions. The peer-review process and scientific exchange of our journals including preprint posting is not affected. However, these restrictions require that the first contact (contractual partner of Copernicus) has an affiliation and invoice address outside Russia or Belarus.
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Geoscientific Model Development
An interactive open-access journal of the European Geosciences Union
All site content, except where otherwise noted, is licensed under the Creative Commons Attribution 4.0 License.
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1 CNY 兑换为 GMD | 将 中国人民币 转换为 Gambian Dalasis | XE
Y 兑换为 GMD | 将 中国人民币 转换为 Gambian Dalasis | XE跳至内容个人商业付款转换器货币数据 API工具货币图表任何货币的趋势汇率通知设置目标汇率并获取通知历史货币汇率查看任何日期的汇率IBAN 计算器搜索并验证 IBAN应用智能手机应用等更多工具资源常见问题解答推荐好友博客汇款技巧货币百科全书货币通讯术语表更多资源登录汇款汇率通知注册汇款汇率通知付款转换器货币数据 API工具货币图表任何货币的趋势汇率通知设置目标汇率并获取通知历史货币汇率查看任何日期的汇率IBAN 计算器搜索并验证 IBAN应用智能手机应用等更多工具资源常见问题解答推荐好友博客汇款技巧货币百科全书货币通讯术语表更多资源登录登录汇款汇率通知注册注册汇款汇率通知1 CNY 兑换为 GMDXe 货币转换器转换汇款图表通知金额1¥从CNY – 中国人民币到GMD – 冈比亚达拉西1.00 中国人民币 =9.4408891 冈比亚达拉西1 GMD = 0.105922 CNY我仅的仅仅器会使用中期市仅仅率。仅仅供参考。您仅款仅不会仅得此仅率。 仅看仅款仅率。中国人民币 兑换为 冈比亚达拉西 — 最近更新时间：2024年3月7日 UTC 10:59將 中国人民币 转换为 冈比亚达拉西CNYGMD1 CNY9.44089 GMD5 CNY47.2044 GMD10 CNY94.4089 GMD25 CNY236.022 GMD50 CNY472.044 GMD100 CNY944.089 GMD500 CNY4,720.44 GMD1,000 CNY9,440.89 GMD5,000 CNY47,204.4 GMD10,000 CNY94,408.9 GMD將 冈比亚达拉西 转换为 中国人民币GMDCNY1 GMD0.105922 CNY5 GMD0.529611 CNY10 GMD1.05922 CNY25 GMD2.64806 CNY50 GMD5.29611 CNY100 GMD10.5922 CNY500 GMD52.9611 CNY1,000 GMD105.922 CNY5,000 GMD529.611 CNY10,000 GMD1,059.22 CNYCNY 兑 GMD 图表1 CNY = 0 GMD 查看完整图表1 中国人民币兑 冈比亚达拉西 统计数据过去 30 天过去 90 天高位这些是过去 30 天和 90 天内的最高汇率。9.45219.5165低这些是过去 30 天和 90 天内的最低汇率。9.34889.3488平均这些是这两种货币在过去 30 天和 90 天的平均汇率。9.41499.4303波动率这些百分比显示了过去 30 天和 90 天内的汇率波动幅度。阅读全文0.17%0.30%货币信息CNY - 中国人民币我们的货币排名显示最热门的 中国人民币 汇率是 CNY 兑 USD 汇率。 中国人民币的货币代码为 CNY。 货币符号为 ¥。More 中国人民币 infoGMD - 冈比亚达拉西我们的货币排名显示最热门的 冈比亚达拉西 汇率是 GMD 兑 USD 汇率。 Gambian Dalasis的货币代码为 GMD。 货币符号为 D。More 冈比亚达拉西 info热门 中国人民币 (CNY) 货币配对将 CNY 转换为 USD将 CNY 转换为 EUR将 CNY 转换为 GBP将 CNY 转换为 JPY将 CNY 转换为 CAD将 CNY 转换为 AUD将 CNY 转换为 CHF将 CNY 转换为 CNY全球最受欢迎的货币工具XE 国际汇款快捷安全地在线汇款。实时跟踪和通知外加灵活的交付和付款选项。付款XE Currency 图表为全球任何货币对创建图表，以查看其货币历史记录。这些货币图表使用实时中期市场汇率，易于使用且非常可靠。查看图表XE 汇率通知需要知道某个货币何时达到特定汇率？“XE 汇率通知”将在所选货币对触及所需汇率时通知您。创建通知XE Currency 数据 API为全球 300 多家公司提供商业级汇率支持了解更多下载 XE 应用查看实时汇率、安全地汇款、设置汇率通知、接收通知等等。扫描这里！超过 7000 万次全球下载量4.5/5, 2200 ratings3,8/5, 9.08 万 ratings4.7/5, 4.15 万 ratings语言简体中文EnglishEnglish (UK)DeutschEspañolFrançaisPortuguêsItalianoSvenska日本語繁體中文العربية汇款在线汇款汇款到印度汇款到巴基斯坦汇款到墨西哥汇款到日本汇款到英国汇款到加拿大汇款至澳大利亚汇款到新西兰向移动钱包汇款安全举报欺诈行为Trustpilot 评论XE 商业商业付款国际商业付款全球商业付款Risk Management企业资源规划货币数据 API 集成联盟推荐合作伙伴计划大规模付款应用汇款和货币应用Android 汇款应用iOS 汇款应用工具与资源博客货币转换器货币图表历史货币汇率货币百科全书货币汇率通知货币通讯IBAN 计算器术语表公司信息关于我们合作关系诚聘英才常见问题解答站点地图法律隐私Cookie 政策Consent Manager汇款重要信息提交投诉可达性© 1995-2024 XE.com IGMD反导系统究竟如何工作_风闻
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GMD反导系统究竟如何工作
席亚洲
独立军事评论员，IT业观察者
公众号：亚洲火车总站2019-03-26 16:57
今天，美国导弹防御局进行了一次洲际导弹拦截试验，在这次“破天荒”的实验中，他们首次尝试用两枚GBI拦截弹拦截一枚来袭洲际导弹靶弹，并取得了成功。其实GBI拦截弹从1999年到今天已经发展了多个型号，最早的是90年代初，用在核裁军中裁撤的“民兵”洲际导弹（PLV）作为助推器进行早期测试。后来，开始使用COSTS低成本商用火箭，该火箭的主要助推器依然是“民兵”和“北极星”的剩余零部件。一直到21世纪初进行的几次试验，都是使用COST火箭进行的，不过这种火箭的性能并不先进，给EKV提供的入轨速度等性能一般般。后来波音研制的OBV助推器在2002年开始用于GBI拦截弹，该导弹源于“塔罗斯XL”运载火箭，而这种运载火箭的技术，和“侏儒”洲际导弹关系密切。使用OBV助推火箭的GBI也是第一种进入实战部署的GBI拦截弹。洛克希德的PLV火箭是最早用来测试EKV的助推器，它脱胎于“民兵3”导弹波音COSTS火箭承担了测试和早期部署的任务到了2010年，美国又首次测试了改为2级火箭的新型OBV运载火箭，由于这种火箭的助推段时间更短，因此部署的灵活性更高。该火箭也就是C2助推器。算是第二代GBI拦截弹的标志，称为C2助推器。而另一方面，2016年，MDA又提出，要研制一种性能提高的三级助推器，可以将C2的电子技术用到旧的火箭上，使其性能有很大的提高，尤其是与地面指挥控制系统的联通性能可以有很大的提高，从而大大提高第一代导弹的拦截率。这样就构成了计划未来使用的C3助推器。换句话来说，到2024年，美国将装备的GBI导弹从导弹助推器上来说，将发展到第三代拦截弹。而目前正在积极试射的，是第二代拦截弹。不过美国的反导系统是一个非常特殊的东西，尚未进行过完整试射的导弹就已经装备了，目前美国的44枚拦截弹里有8枚是使用C2助推器的BLOCK II型。去年进行首次洲际导弹拦截试验的也是它。目前GBI拦截弹主力使用OBV火箭，在它基础上又发展出C2\C3火箭，进一步提高反应速度目前尚不清楚今天进行试验的导弹是OBV C1还是C2，不过从需要用齐射提高命中率来看，很可能是在之前的测试中被发现实际拦截成功率大概只有50%的C1。不过之前GBI导弹拦截率不高的锅，主要还真不是火箭的事儿——毕竟美国的商用运载火箭技术那是领先世界，非常成熟，搞不好的概率并不高。真正的问题在于拦截器。目前美国的大气层外动能拦截器——EKV也已经发展到了第三种改进型，该拦截器可不一般，它是由波音、洛马、雷神、轨道科学等军火巨头，在美国政府的组织下，“集中力量办大事”造出来的。不过当然了，这就导致它出了问题以后，责任是谁的大家也要争执好久。目前使用的EKV，虽然几经改进性能有了不少提高，但美军仍计划用新型RKV将其取代RKV上运用了标准3导弹上的一些技术，性能比EKV提高多了当然最终的目标是MOKV，多目标拦截才是美军的梦想不过甭管怎样吧，到了最新的改进型CE-II blcok II，该拦截器的成功率已经比此前型号提高了不少。但MDA方面早在2015年就已经规划第二代拦截器，RKV（重新设计的动能杀伤器），RKV最早被称为CE-III block I，它的探测视场将比第一代的EKV有较大幅度的提升，而且尺寸可能还会减小，将会运用很多雷神公司在标准3导弹的大气层外动能拦截器上运用的新型技术。当然，美军还有一个远期目标，即在RKV基础上进一步缩小尺寸，提高性能，造出一枚GBI导弹能携带多个的MOKV多目标拦截器，从而实现更好的拦截大量来袭目标的能力。从这个角度来看，GBI拦截弹的大气层外杀伤器目前正在第二代产品开发的关键阶段。不过，很多读者虽然知道GBI导弹和GMD系统的名称，也知道了它们的拦截方式——将大气层外拦截器（实际上是一系列杀手卫星）用洲际导弹尺寸的拦截弹，投射到敌方洲际导弹飞行路线上，在中段飞行阶段实施轨道拦截。拦截弹上的高灵敏度红外探测器在这一阶段将自动搜索和引导杀伤器与目标进行直接碰撞。但这些导弹作战的具体过程如何呢？这其中有没有什么可被利用的缺陷呢？其实还真有，我们得先了解一下GBI拦截的工作过程。关于GBI的具体工作流程，大致描述如下：（1）早期预警阶段该阶段对应弹道导弹的初段飞行。弹道导弹发射后，部署在地球轨道上的预警卫星“国防支援计划”（DSP）或“天基红外系统”（SBIRS）探测到弹道导弹发射时助推器的尾焰，对其进行跟踪直到弹道导弹助推火箭关机为止。预警卫星获取的信息通过中继卫星和地面站传送回作战管理与指挥控制通信（BMC3）系统，在其中进行分析处理，预报弹道导弹来袭方向和落区，并将相应数据发送到早期预警雷达。预警雷达获取弹道导弹方位信息后，对相关空域进行搜索，探测并跟踪来袭导弹；在发现导弹弹头后进行跟踪，稳定跟踪后向BMC3（指挥、控制、战斗管理计算机系统）系统传送信息。（2）拦截决策阶段该阶段对应弹道导弹的中段飞行。BMC3系统根据预警雷达的探测跟踪信息制定作战管理规划，包括确定拦截方式、拦截弹的数量，进行约束条件判断，如阳光是否会使大气层外拦截器（EKV）的红外导引头致盲、地基拦截弹和EKV的有效作用距离等，初步确定地基拦截弹的发射方位和发射时刻。同时获得发射地基拦截弹的授权。BMC3系统将预警雷达的探测跟踪信息发送到地基雷达或前沿部署的X波段雷达，引导地基雷达进行搜索。地基雷达通过对弹道导弹弹头的探测和跟踪可以获取更加精确的信息，为BMC3系统的拦截决策提供数据。同时，地基雷达利用收集到的足够多的识别数据生成弹头的“目标实物图”。BMC3系统利用地基雷达提供的信息进行目标识别和威胁判断，如导弹弹头类型的确认和对弹头弹道和落点的精确预报。在此基础上，进行拦截决策，确定地基拦截弹的发射时刻和预估拦截遭遇点。当预报的拦截遭遇点精度达到指定的范围（20公里）时，BMC3系统下达发射地基拦截弹的指令，预报的拦截遭遇点即为地基拦截弹的瞄准点。（3）拦截实施阶段本阶段和四、五阶段对应弹道导弹的末段飞行。在接到地基拦截弹（GBI）发射的命令后，GBI立即发射（可选择每次发射一枚地基拦截弹或同时发射两枚地基拦截弹）。GBI发射后，地基雷达和前沿部署的X波段雷达对GBI和弹道导弹弹头进行精确跟踪，并通过BMC3系统中的“飞行中拦截弹通讯系统”（IFICS）为GBI提供目标的修正数据，引导GBI飞行。在天基红外系统（SBIRS）部署之后，SBIRS的低轨卫星可以为GBI提供制导信息。（当然SBIRS系统目前正处于扯淡阶段，可能在它基础上进一步发展，增加探测高超声速飞行目标能力）当GBI飞行到距离弹道导弹弹头一定距离，即进入EKV导引头能够探测到弹头的交战空域时，EKV与GBI的助推火箭分离。当EKV的红外探测器探测到威胁目标时，它将探测到的目标信息与前期由雷达提供的“目标实物图”信息进行比较和识别，确认并锁定真实弹头，并以直接碰撞的方式将弹头予以摧毁。（4）拦截效果评估在拦截交战过程中，地基雷达及前沿部署的X波段雷达负责跟踪搜集拦截信息，为BMC3系统提供拦截杀伤的评价信息。BMC3进行拦截交战的杀伤效果进行最终评判，如果拦截未成功，则决定是否需要对弹道导弹弹头实施第二次拦截。（5）毁伤效果评估如果导弹被拦截，则导弹对目标没有造成毁伤。如果进攻方导弹最终突防成功，则要对进攻方导弹对目标的毁伤效果做出评估。在导弹攻防对抗中，当预警卫星和预警雷达完成对目标的预警后，BMC3会引导雷达会对目标进行跟踪，完成雷达的稳定跟踪和BMC3对目标的预测拦截点解算以后，BMC3下达GBI发射命令，GBI发射并对目标进行拦截。GBI拦截目标的工作过程如下：GBI接收到BMC3的发射命令后立即发射并进入助推段，助推段完成后到达关机点。关机点的坐标位置和速度矢量根据发射命令中的相关信息确定。到达关机点后GBI进入自由飞行阶段。在自由飞行阶段，GBI只在重力的作用下飞行。BMC3会根据雷达提供的信息向GBI发送制导指令对GBI弹道进行修正，GBI进入指令制导段。在自由飞行段或者指令制导段末期，当GBI与目标的距离到达一定值以后，BMC3向GBI发来交班指令；GBI按照交班指令中的目标信息对指定空域进行扫描，如果目标不在GBI的视场内，则交班失败，该枚拦截弹工作结束；若在其视场内则交班成功，释放EKV进入末制导阶段。在末制导阶段，GBI根据目标队列中各个目标的红外特性，进行目标识别，确定拦截目标，按照一定的制导规律引导导弹向该目标飞行，最后对毁伤目标的结果做出评估。在整个GBI拦截过程中，进攻导弹会采取一系列的突防措施如电子干扰、机动和施放诱饵等来降低被拦截的概率。了解了GBI拦截弹的拦截流程，我们可以发现，整个反导拦截系统是一个非常复杂，中间有大量可能出错，导致拦截失败的环节。首先，它的起飞阶段，需要地面的预警雷达尽早发现，并且要有足以对来袭目标进行高精度成像的雷达来对目标进行识别。在来袭导弹飞行到中段的时候，其已经离开大气层，发动机关机，多弹头导弹的弹头也已经和制导舱分离，此时弹头完全按照惯性飞行，在结束中段飞行再入大气层之前是不会进行机动的。（这是NMD系统拦截的一个基本想定，如果弹头在中段能进行机动，GBI很难跟上）当然这样一个复杂的拦截过程，也确实是更接近于一场航天科研行动，而不像是作战在这一前提下，陆基雷达精确跟踪目标，并对目标性质进行分辨，确定一个拦截弹和来袭弹头的理想“汇合点”，将GBI导弹发射到这个点去。在GBI导弹到达这个点之前，它实际上和弹道导弹是一样的，无法对这个“汇合点”进行修改。在抵达“汇合点”后，GBI导弹发出EKV，此时EKV进入指令制导阶段，它将接收地面雷达对目标测绘后绘制的高精度3D模型数据，通过这个模型数据，将其高灵敏度红外探测器发现的目标图像和3D模型各个方向图像进行对比，找到它要攻击的目标，然后开始进行复杂的变轨机动，并与目标进行高速碰撞。整个的工作过程，要依赖天地通信，地面高精度测绘，EKV本身的高精度红外探测系统和自动控制系统，大气层外的精确变轨能力，以及EKV与卫星之间的通信等等。在整个过程中还要考虑来袭弹头除了发射气球假目标，还会进行电子对抗，由于EKV距离弹头目标距离远远小于与地面的距离，所以即使不是很大功率的干扰信号，也有可能破坏EKV的天地通信，从而导致拦截失败。更不用说，如果来袭目标采用全程机动技术（俄罗斯声称在“亚尔斯”和“萨尔玛特”上要运用这个技术），或者干脆就是在两三万米的临近空间飞行，那GBI拦截弹就基本傻眼了。从这一点我们也可以看出，GBI对于上一代的洲际导弹，确实可以达到较高的拦截率，而且对当时常用的气球诱饵、干扰无线电指令等技术也有了完善的对抗手段。而且如果2026年美国如期装备MOKV，那么即使是多弹头也不再是对美国GBI系统突防的灵丹妙药。在这种情况下，加强洲际导弹突防能力对于中俄等国来说就变得非常重要了。当然了，既然了解了GBI的工作流程，那么……其实对于掌握太空对接技术的国家来说，造出自己的NMD系统和GBI导弹来，也无非是金钱和时间的问题……俄罗斯现在没钱，中国在时间上起步有点晚，所以GBI目前还能再占据世界独一份的制高点一段时间，不过随着中国陆基反导雷达网部署进度加快，DN系列导弹的新测试什么时候开始呢？咱们拭目以待。
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地学系硕士生杨昊禹在《Geoscientific Model Development》发文介绍一种新的水平网格并行三角化算法-清华大学地球系统科学系
地学系硕士生杨昊禹在《Geoscientific Model Development》发文介绍一种新的水平网格并行三角化算法-清华大学地球系统科学系
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地学系硕士生杨昊禹在《Geoscientific Model Development》发文介绍一种新的水平网格并行三角化算法
2019年08月19日 00:00:00
7月26日，地学系耦合器组硕士毕业生杨昊禹为第一作者，在地学高影响期刊《Geoscientific Model Development》（GMD）上发表题为“PatCC1: an efficient parallel triangulation algorithm for spherical and planar grids with commonality and parallel consistency”的研究论文。地学系刘利副教授、博士后李锐喆作为该文通讯作者，文章合作者还包括地学系博士后张诚，博士生孙超、于馨竹、于灏，王斌教授和水文气象中心的张志远博士。
图：PatCC1算法流程图
该论文介绍了地学系耦合器研究组最新研制的水平网格并行三角化算法PatCC1（三角化是计算机图形学的基础算法，也经常应用于地球系统模式领域），其特色在于：不仅比国际上已有并行三角化算法运行快很多（在相同并行度下），而且同时保证了最好通用性（能处理各种水平网格）和并行一致性（在不同并行设置下的三角化结果完全相同，避免了其他并行三角化算法并行剖分边界拼接的问题）。算法源代码（可通过文中链接下载）已通过了上万个用例的严格测试，具有较大的实用意义。
地学系耦合器研究组从2010年开始专注于地球系统模式耦合器这一关键核心技术的自主研发，于2018年成功研制了国产耦合器版本C-Coupler2（其论文也发表在GMD期刊上）。C-Coupler2具有完备的功能和相对于欧美耦合器的多项优势，已应用于国家气象中心、国家海洋环境预报中心、国家气候中心、海军、自然资源部第一海洋研究所、中国科学院大气物理研究所、清华大学等科研单位，以及多个国家重点研发计划项目的耦合模式研发。C-Coupler2被由我国11位著名地学科学家所组成专家组评价为“具有国际领先水平”、“使得我国模式发展打破了必须依赖于国外耦合器的不利局面，对推动我国地球系统科学和相关的业务发展具有重大意义”。PatCC1算法将应用于C-Coupler未来版本的研制，进一步改进其中的网格管理和插值功能。
PatCC1算法的论文发表在GMD期刊的模式框架集成与互操作性（MI3）专刊上。自2015年以来，该专刊已发表了9篇论文，其中4篇由地学系耦合器研究组发表。
论文链接：
https://www.geosci-model-dev.net/special_issue598.html
上一篇：地学系硕士生曹超纪发文《应用能源》揭示不同地区减排相同量的二氧化碳带来的健康协同效益存在差异
下一篇：清华地学系张强研究组合作发文揭示全球现有能源基础设施锁定排放威胁1.5℃温控目标
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七位知名学者做客我校“前沿科学报告”
2022年04月29日 12:50 沙桐 李小飞 点击：[]
4月23日下午，应环境科学与工程学院和科技处邀请，南方科技大学杨新教授、复旦大学王琳教授、中科院大气所孙业乐研究员、中科院广州地化所丁翔研究员、北京大学郭松研究员、山东大学薛丽坤教授、清华大学刘欢教授做客我校“前沿科学报告”，环境学院及其全国其他兄弟院校、科研院所师生500余人参加了报告会。会议由环境学院陈庆彩教授和山东大学杜林教授主持。
杨新教授认为，尽管我国整体大气环境质量有所提高，但颗粒物污染依旧影响着很多城市空气质量，并对人体健康造成一定危害。他的团队基于小分子化学探针和颗粒物与人体血清白蛋白结合作用这两个角度评估了PM2.5组分活性氧生成潜势及健康效应，有效拓展了PM2.5毒性研究结果。
王琳教授详细介绍了具有极低挥发性和高氧化度的人为源有机化合物的生成机制及其在中国城市和郊区大气中的时空变化特征，并建立了一种估算颗粒物增长速率的方法，研究成果对新粒子形成的研究具有很好的推动作用。
孙业乐研究员以“汾河平原秋冬季气溶胶组分来源、变化特征及光学特性”为题，通过对汾河平原秋冬季进行综合观测试验，详细介绍了该地区细颗粒物化学组成、来源、变化及光学特性。他指出，汾渭平原已经成为我国污染最为严重的地区之一，对该地区气溶胶理化性质进行综合的长期观测对空气质量的改善具有重要意义。
丁翔研究员指出，异戊二烯作为SOA的重要前体物，对厘清城市地区异戊二烯二次SOA的前体物来源、生成机制非常重要。污染背景下异戊二烯SOA以低NOx产物途径生成为主，并且低NOx成因特征在全国污染大气中具有普遍性。他强调，减少人为源排放可实现颗粒物和臭氧的协同控制。
郭松研究员以“生活方式源排放半/中等挥发性有机物对二次颗粒物贡献研究”为题，介绍了关键典型源排放经二次转化对SOA生成的贡献。他强调在相同排放条件下，机动车排放源生成SOA潜力最大，餐饮和生物质燃烧次之。他认为，紧急防控污染时，对汽油车排放的控制可能会有更加显著的效果，而餐饮排放管控对长期SOA控制更重要。
薛丽坤教授以中国东部地区夏季臭氧污染特征与机制分析为主题进行了报告，她指出，遵循NOx和VOC最优比例减排、动态调整，可实现最优的臭氧控制策略。她强调，臭氧水平对硝酸盐生成具有重要影响，控制臭氧污染可有效降低硝酸盐浓度。
刘欢教授的团队开发了包含两个“自下而上”海运贸易排放评估模型的复合技术框架（VoySEIM-GTEMS），首次在全球尺度实现了海运排放与贸易驱动力的定量关联，为引导国际合作促进海运碳减排提供方法学基础和量化模型支撑。
七位专家的报告内容丰富，开阔了广大师生的视野。参会师生普遍认为，此次报告对相关领域开展科研工作有深刻的启发性，使大家受益匪浅。
新闻小贴士：
杨新，南方科技大学环境学院院长、教授，教育部特聘教授、国务院特殊津贴专家，担任Atmospheric Environment副主编、中国化学学会环境化学专业委员会委员等学术职务。
王琳，复旦大学环境科学与工程系系主任、教授，国家杰出青年基金获得者。先后获得国家优秀青年基金、海外高层次人才引进计划、英国皇家学会牛顿高级学者、英国皇家化学会会士等人才荣誉。
孙业乐，中科院大气物理研究所研究员，科技部中青年科技创新领军人才。曾获赵九章优秀中青年科学奖、陈嘉庚青年科学奖、中国科学院青年科学家奖等。
丁翔，中科院广州地化所研究员，国家优秀青年基金获得者，粤港澳环境污染过程与控制联合实验室副主任。
郭松，北京大学环境学院研究员，先后荣获国家自然科学奖二等奖、教育部自然科学奖一等奖，获2020年国家环境保护专业技术青年拔尖人才。
薛丽坤，山东大学环境研究院副院长、教授，国家优秀青年基金获得者，中国环境科学学会大气环境分会副秘书长、臭氧污染控制专业委员会常务委员
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地学系黄小猛副教授研究组在《Geoscientific Model Development》发文-清华大学地球系统科学系
地学系黄小猛副教授研究组在《Geoscientific Model Development》发文-清华大学地球系统科学系
清华主页
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点点萤火，汇聚星河 ——清华大学地学系抗疫突击队工作记
清华大学地学系白玉琪教授赴加纳参加地球观测组织年会
喜报！清华大学地学系“计算地球科学团队”荣获“清华大学先进集体”称号
新闻热点
2023.06.25
磨砺以须出锋芒，倍道兼进正当时 | 清华大学地球系统科学系主任罗勇在2023届地学系研究生毕业典礼上的致辞
2023.06.25
清华大学地球系统科学系举行2023年研究生毕业典礼
2023.01.03
点点萤火，汇聚星河 ——清华大学地学系抗疫突击队工作记
2022.12.21
清华大学地学系白玉琪教授赴加纳参加地球观测组织年会
2022.09.16
喜报！清华大学地学系“计算地球科学团队”荣获“清华大学先进集体”称号
地学系黄小猛副教授研究组在《Geoscientific Model Development》发文
2019年11月21日 00:00:00
11月12日，地学系黄小猛副教授研究组在地学高影响期刊《Geoscientific Model Development》（GMD）上发表题为“OpenArray v1.0: a simple operator library for the decoupling of ocean modeling and parallel computing”的论文，文章提出一种高效自动并行的模式开发框架OpenArray，基于OpenArray构建三维海洋模式GOMO，实现了海洋模式和并行计算的解耦，该工作为海洋模式开发提供了一种新思路。
海洋模式研发是一项复杂的工作，需要研究人员具备扎实的领域知识、数理基础和计算机并行编程能力，而模式并行程序结构复杂，难于编写，而且模式计算平台也日益复杂，如何在多种架构平台（例如CPU、GPU和Sunway）上实现高效模式的开发和应用是模式社区面临的挑战。
本文采用计算中间件的思想，开发自动并行算子库OpenArray，为模式开发人员提供12个简洁且自动并行的基本算子求解偏微分方程，把繁琐的模式并行计算进行封装，达到“方程即代码”的效果。
基于OpenArray，开发区域海洋模式GOMO，模式1860行代码，支持通用CPU和神威平台。经计算图、融核和通信隐藏等多种方法优化后，GOMO代码能达到与手写MPI同等的并行效率，并在神威平台上达到20万核的68%扩展性能。
图GOMO的计算效率和扩展性.图(a)和(b)分别是GOMO和sbPOM强扩展性和弱扩展性对比，图(c)是GOMO在神威平台上的扩展性。
地学系黄小猛副教授作为该文第一作者和通讯作者，文章合作者还包括地学系博士生黄兴、王冬、王明清、唐强、陈悦、方正，硕士生吴琦、陈昱文，李熠博士后，杨广文教授和海洋一所的宋振亚研究员。
论文链接：
https://www.geosci-model-dev.net/12/4729/2019/
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GMD - Definition by AcronymFinder
GMD - Definition by AcronymFinder
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What does GMD stand for?
Your abbreviation search returned 41 meanings
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Information Technology (4)
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Science & Medicine (16)
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GMDGambian Dalasi (ISO currency code)
GMDGeometry Dash
GMDGearless Mill Drive (mining)
GMDGand Mein Danda (song)
GMDGame Mode
GMDGround-based Midcourse Defense (US DoD)
GMDGreat Mouse Detective
GMDGolm Metabolome Database (biology database; Germany)
GMDGlobal Monitoring Division (US NOAA)
GMDGestational Diabetes Mellitus (disease)
GMDGeneral Material Designation
GMDGlobal Medical Directory (est. 2008)
GMDGeometric Mean Distance
GMDGlobal Missile Defense
GMDGeneralized Minimum Distance
GMDGrupo Moçambicano da Dívida (Portuguese: Mozambican Debt Group; Mozambique)
GMDGesellschaft für Mathematik und Datenverarbeitung mbH (Bonn, Germany; Society for Mathematics and Data Processing)
GMDGround-Based Missile Defense
GMDGini's Mean Difference (statistics)
GMDGlobal Markets Division (Ohio Department of Development)
GMDGraphic Multimedia Design (various locations)
GMDGolden Mean Design
GMDGrid Market Directory
GMDGeometric Mean Diameter
GMDGreen Mountain Daily (political blog)
GMDGeo-Magnetic Disturbance (North American Electric Reliability Corporation)
GMDGeology and Mining Department
GMDGeneral Motors Division
GMDGeneral Militia District
GMDGuo Ming Dang (Chinese Nationalist Party, Taiwan)
GMDGaming Machine Duty (tax; New Zealand)
GMDGuided Missile Division
GMDGuaranteed Message Delivery
GMDGo and Make Disciples
GMDGenetic Multiuser Detector
GMDGround Meteorological Detector
GMDGeometrodynamics
GMDGeneral Management Directive
GMDGrupo Mexico de Desarrollo (American Depository Receipts) (stock symbol)
GMDGovernment Maintenance Depot
GMDGeneral Medical Corporation (former stock symbol; now delisted)
Note: We have 82 other definitions for GMD in our Acronym Attic
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GMCSCO
GMCSF
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GMCU
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GMDA
GMDAT
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GMDE
GMDECB
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