Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5511-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-5511-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Jul 2022
Development and technical paper |
| 19 Jul 2022
| 19 Jul 2022
Improving the joint estimation of CO2 and surface carbon fluxes using a constrained ensemble Kalman filter in COLA (v1.0)
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Earth System Science Interdisciplinary Center, College Park, Maryland, USA
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Yun Liu
International Laboratory for High-Resolution Earth System Model and
Prediction (iHESP), Texas A&M University, College Station, Texas, USA
Department of Oceanography, Texas A&M University, College Station, TX, USA
Eugenia Kalnay
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Ghassem Asrar
Universities Space Research Association, Columbia, Maryland, USA
Bo Wu
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Qixiang Cai
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Di Liu
Research Center for Eco-Environmental Sciences, Chinese Academy of
Sciences, Beijing, China
Pengfei Han
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
State Key Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
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Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
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Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
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Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Qixiang Cai, and Pengfei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-15, https://doi.org/10.5194/gmd-2023-15, 2023
Revised manuscript not accepted
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Wenxiu Zhang, Di Liu, Hanqin Tian, Naiqin Pan, Ruqi Yang, Wenhan Tang, Jia Yang, Fei Lu, Buddhi Dayananda, Han Mei, Siyuan Wang, and Hao Shi
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Ruqi Yang, Jun Wang, Ning Zeng, Stephen Sitch, Wenhan Tang, Matthew Joseph McGrath, Qixiang Cai, Di Liu, Danica Lombardozzi, Hanqin Tian, Atul K. Jain, and Pengfei Han
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Tao Zhang, Yuyu Zhou, Zhengyuan Zhu, Xiaoma Li, and Ghassem R. Asrar
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Yang Yang, Minqiang Zhou, Ting Wang, Bo Yao, Pengfei Han, Denghui Ji, Wei Zhou, Yele Sun, Gengchen Wang, and Pucai Wang
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Di Liu, Wanqi Sun, Ning Zeng, Pengfei Han, Bo Yao, Zhiqiang Liu, Pucai Wang, Ke Zheng, Han Mei, and Qixiang Cai
Atmos. Chem. Phys., 21, 4599–4614, https://doi.org/10.5194/acp-21-4599-2021, https://doi.org/10.5194/acp-21-4599-2021, 2021
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It is difficult to directly observe the COVID-19 signals in CO2 due to the strong weather induced variations. Here, we determined the on-road CO2 concentration declines in Beijing using mobile observatory data before (BC), during (DC) and after COVID-19 (AC). We chose trips with the most similar weather and calculated the enhancement, the difference between on-road and the city “background”. We showed a clear on-road CO2 decrease in DC, which is consistent with the emissions reductions in DC.
Xiaohui Lin, Wen Zhang, Monica Crippa, Shushi Peng, Pengfei Han, Ning Zeng, Lijun Yu, and Guocheng Wang
Earth Syst. Sci. Data, 13, 1073–1088, https://doi.org/10.5194/essd-13-1073-2021, https://doi.org/10.5194/essd-13-1073-2021, 2021
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CH4 is a potent greenhouse gas, and China’s anthropogenic CH4 emissions account for a large proportion of global total emissions. However, the existing estimates either focus on a specific sector or lag behind real time by several years. We collected and analyzed 12 datasets and compared them to reveal the spatiotemporal changes and their uncertainties. We further estimated the emissions from 1990–2019, and the estimates showed a robust trend in recent years when compared to top-down results.
Pengfei Han, Ning Zeng, Tom Oda, Xiaohui Lin, Monica Crippa, Dabo Guan, Greet Janssens-Maenhout, Xiaolin Ma, Zhu Liu, Yuli Shan, Shu Tao, Haikun Wang, Rong Wang, Lin Wu, Xiao Yun, Qiang Zhang, Fang Zhao, and Bo Zheng
Atmos. Chem. Phys., 20, 11371–11385, https://doi.org/10.5194/acp-20-11371-2020, https://doi.org/10.5194/acp-20-11371-2020, 2020
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An accurate estimation of China’s fossil-fuel CO2 emissions (FFCO2) is significant for quantification of carbon budget and emissions reductions towards the Paris Agreement goals. Here we assessed 9 global and regional inventories. Our findings highlight the significance of using locally measured coal emission factors. We call on the enhancement of physical measurements for validation and provide comprehensive information for inventory, monitoring, modeling, assimilation, and reducing emissions.
Dalei Hao, Ghassem R. Asrar, Yelu Zeng, Qing Zhu, Jianguang Wen, Qing Xiao, and Min Chen
Earth Syst. Sci. Data, 12, 2209–2221, https://doi.org/10.5194/essd-12-2209-2020, https://doi.org/10.5194/essd-12-2209-2020, 2020
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We adopted machine-learning models to generate the first global land products of SW–PAR based on DSCOVR/EPIC data. Our products are consistent with ground-based observations, capture the spatiotemporal patterns well and accurately track substantial diurnal, monthly and seasonal variations in SW–PAR. Our products provide a valuable alternative for solar photovoltaic applications and can be used to improve our understanding of the diurnal cycles of terrestrial water, carbon and energy fluxes.
Binghao Jia, Xin Luo, Ximing Cai, Atul Jain, Deborah N. Huntzinger, Zhenghui Xie, Ning Zeng, Jiafu Mao, Xiaoying Shi, Akihiko Ito, Yaxing Wei, Hanqin Tian, Benjamin Poulter, Dan Hayes, and Kevin Schaefer
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use and land cover change, and elevated CO2 to interannual variations and seasonal cycle amplitude of gross primary productivity (GPP) in China based on multi-model ensemble simulations. The contributions of major subregions to the temporal change in China's total GPP are also presented. This work may help us better understand GPP spatiotemporal patterns and their responses to regional changes and human activities.
Yun Liu, Eugenia Kalnay, Ning Zeng, Ghassem Asrar, Zhaohui Chen, and Binghao Jia
Geosci. Model Dev., 12, 2899–2914, https://doi.org/10.5194/gmd-12-2899-2019, https://doi.org/10.5194/gmd-12-2899-2019, 2019
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We developed a new carbon data assimilation system to estimate the surface carbon fluxes using the LETKF and GEOS-Chem model, which uses a new scheme with a short
assimilation windowand a long
observation window. The analysis is more accurate using the short assimilation window and is exposed to the future observations that accelerate the spin-up. In OSSE, the system reduces the analysis error significantly, suggesting that this method could be used for other data assimilation problems.
Jun Wang, Ning Zeng, Meirong Wang, Fei Jiang, Jingming Chen, Pierre Friedlingstein, Atul K. Jain, Ziqiang Jiang, Weimin Ju, Sebastian Lienert, Julia Nabel, Stephen Sitch, Nicolas Viovy, Hengmao Wang, and Andrew J. Wiltshire
Atmos. Chem. Phys., 18, 10333–10345, https://doi.org/10.5194/acp-18-10333-2018, https://doi.org/10.5194/acp-18-10333-2018, 2018
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Based on the Mauna Loa CO2 records and TRENDY multi-model historical simulations, we investigate the different impacts of EP and CP El Niños on interannual carbon cycle variability. Composite analysis indicates that the evolutions of CO2 growth rate anomalies have three clear differences in terms of precursors (negative and neutral), amplitudes (strong and weak), and durations of peak (Dec–Apr and Oct–Jan) during EP and CP El Niños, respectively. We further discuss their terrestrial mechanisms.
Guo-Yuan Lien, Daisuke Hotta, Eugenia Kalnay, Takemasa Miyoshi, and Tse-Chun Chen
Nonlin. Processes Geophys., 25, 129–143, https://doi.org/10.5194/npg-25-129-2018, https://doi.org/10.5194/npg-25-129-2018, 2018
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The ensemble forecast sensitivity to observation (EFSO) method can efficiently clarify under what conditions observations are beneficial or detrimental for assimilation. Based on EFSO, an offline assimilation method is proposed to accelerate the development of data selection strategies for new observing systems. The usefulness of this method is demonstrated with the assimilation of global satellite precipitation data.
Jun Wang, Ning Zeng, Meirong Wang, Fei Jiang, Hengmao Wang, and Ziqiang Jiang
Earth Syst. Dynam., 9, 1–14, https://doi.org/10.5194/esd-9-1-2018, https://doi.org/10.5194/esd-9-1-2018, 2018
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Behaviors of terrestrial ecosystems differ in different El Niños. We analyze terrestrial carbon cycle responses to two extreme El Niños (2015/16 and 1997/98), and find large differences. We find that global land–atmosphere carbon flux anomaly was about 2 times smaller in 2015/16 than in 1997/98 event, without the obvious lagged response. Then we illustrate the climatic and biological mechanisms of the different terrestrial carbon cycle responses in 2015/16 and 1997/98 El Niños regionally.
Yun Liu, Eugenia Kalnay, Ning Zeng, Ghassem Asrar, Zhaohui Chen, and Binghao Jia
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-888, https://doi.org/10.5194/acp-2017-888, 2017
Preprint withdrawn
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We developed a new Carbon data assimilation system to estimate the surface carbon fluxes using the LETKF and GEOS-Chem model, which uses a new scheme with a short
assimilation windowand a long
observation window. The analysis is more accurate with the short assimilation window and is exposed to the future observations accelerating the spin up. In OSSE, the system reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.
Pengfei Han, Ning Zeng, Fang Zhao, and Xiaohui Lin
Earth Syst. Dynam., 8, 875–887, https://doi.org/10.5194/esd-8-875-2017, https://doi.org/10.5194/esd-8-875-2017, 2017
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Global cropland net primary production has tripled over the last 50 years. However, long-term comparisons across global croplands are scarce due to the lack of detailed management data. Here, we conducted a simulation study of global cropland production from 1961 to 2010 using the VEGAS model. We modified the key parameter associated with the Green Revolution. The updated results decreased the RMSE by ~ 45 %, suggesting it is important to calibrate key parameters on regional scales.
Cory R. Martin, Ning Zeng, Anna Karion, Russell R. Dickerson, Xinrong Ren, Bari N. Turpie, and Kristy J. Weber
Atmos. Meas. Tech., 10, 2383–2395, https://doi.org/10.5194/amt-10-2383-2017, https://doi.org/10.5194/amt-10-2383-2017, 2017
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A low-cost sensor for measuring carbon dioxide is evaluated for its performance in detecting concentrations in Earth's atmosphere. After a multivariate regression correcting for environmental variables, the root mean square error between it and a research-grade gas analyzer is less than 0.5 % of the observed average value. This demonstrates the viability for using these sensors in certain real-world atmospheric observing applications.
Tianjun Zhou, Andrew G. Turner, James L. Kinter, Bin Wang, Yun Qian, Xiaolong Chen, Bo Wu, Bin Wang, Bo Liu, Liwei Zou, and Bian He
Geosci. Model Dev., 9, 3589–3604, https://doi.org/10.5194/gmd-9-3589-2016, https://doi.org/10.5194/gmd-9-3589-2016, 2016
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This paper tells why to launch the Global Monsoons Model Inter-comparison Project (GMMIP) and how to achieve its scientific goals on monsoon variability. It addresses the scientific questions to be answered, describes three tiered experiments comprehensively and proposes a basic analysis framework to guide future research. It will help the monsoon research communities to understand the objectives of the GMMIP and the modelling groups involved in the GMMIP conduct the experiments successfully.
Fang Zhao, Ning Zeng, Ghassem Asrar, Pierre Friedlingstein, Akihiko Ito, Atul Jain, Eugenia Kalnay, Etsushi Kato, Charles D. Koven, Ben Poulter, Rashid Rafique, Stephen Sitch, Shijie Shu, Beni Stocker, Nicolas Viovy, Andy Wiltshire, and Sonke Zaehle
Biogeosciences, 13, 5121–5137, https://doi.org/10.5194/bg-13-5121-2016, https://doi.org/10.5194/bg-13-5121-2016, 2016
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The increasing seasonality of atmospheric CO2 is strongly linked with enhanced land vegetation activities in the last 5 decades, for which the importance of increasing CO2, climate and land use/cover change was evaluated in single model studies (Zeng et al., 2014; Forkel et al., 2016). Here we examine the relative importance of these factors in multiple models. Our results highlight models can show similar results in some benchmarks with different underlying regional dynamics.
Erin Lynch, Daniel Kaufman, A. Surjalal Sharma, Eugenia Kalnay, and Kayo Ide
Nonlin. Processes Geophys., 23, 137–141, https://doi.org/10.5194/npg-23-137-2016, https://doi.org/10.5194/npg-23-137-2016, 2016
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In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the nearest-neighbor bred vectors are shown to be similar to bred vectors computed using evolution equations, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.
G. Murray-Tortarolo, P. Friedlingstein, S. Sitch, V. J. Jaramillo, F. Murguía-Flores, A. Anav, Y. Liu, A. Arneth, A. Arvanitis, A. Harper, A. Jain, E. Kato, C. Koven, B. Poulter, B. D. Stocker, A. Wiltshire, S. Zaehle, and N. Zeng
Biogeosciences, 13, 223–238, https://doi.org/10.5194/bg-13-223-2016, https://doi.org/10.5194/bg-13-223-2016, 2016
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We modelled the carbon (C) cycle in Mexico for three different time periods: past (20th century), present (2000-2005) and future (2006-2100). We used different available products to estimate C stocks and fluxes in the country. Contrary to other current estimates, our results showed that Mexico was a C sink and this is likely to continue in the next century (unless the most extreme climate-change scenarios are reached).
C. Le Quéré, R. Moriarty, R. M. Andrew, G. P. Peters, P. Ciais, P. Friedlingstein, S. D. Jones, S. Sitch, P. Tans, A. Arneth, T. A. Boden, L. Bopp, Y. Bozec, J. G. Canadell, L. P. Chini, F. Chevallier, C. E. Cosca, I. Harris, M. Hoppema, R. A. Houghton, J. I. House, A. K. Jain, T. Johannessen, E. Kato, R. F. Keeling, V. Kitidis, K. Klein Goldewijk, C. Koven, C. S. Landa, P. Landschützer, A. Lenton, I. D. Lima, G. Marland, J. T. Mathis, N. Metzl, Y. Nojiri, A. Olsen, T. Ono, S. Peng, W. Peters, B. Pfeil, B. Poulter, M. R. Raupach, P. Regnier, C. Rödenbeck, S. Saito, J. E. Salisbury, U. Schuster, J. Schwinger, R. Séférian, J. Segschneider, T. Steinhoff, B. D. Stocker, A. J. Sutton, T. Takahashi, B. Tilbrook, G. R. van der Werf, N. Viovy, Y.-P. Wang, R. Wanninkhof, A. Wiltshire, and N. Zeng
Earth Syst. Sci. Data, 7, 47–85, https://doi.org/10.5194/essd-7-47-2015, https://doi.org/10.5194/essd-7-47-2015, 2015
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Carbon dioxide (CO2) emissions from human activities (burning fossil fuels and cement production, deforestation and other land-use change) are set to rise again in 2014.
This study (updated yearly) makes an accurate assessment of anthropogenic CO2 emissions and their redistribution between the atmosphere, ocean, and terrestrial biosphere in order to better understand the global carbon cycle, support the development of climate policies, and project future climate change.
F. Zhao and N. Zeng
Earth Syst. Dynam., 5, 423–439, https://doi.org/10.5194/esd-5-423-2014, https://doi.org/10.5194/esd-5-423-2014, 2014
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This paper presents the CMIP5 model predictions on the seasonal characteristics of global carbon cycle. We show a model consensus that the amplitude of this seasonal cycle will increase in the future under the RCP8.5 emission scenario. This is mostly due to enhanced ecosystem productivity in high latitude regions. While the models' ensemble CO2 amplitude increase is close to observation, our results suggest the underlying mechanisms may not be realistic.
J. B. Fisher, M. Sikka, W. C. Oechel, D. N. Huntzinger, J. R. Melton, C. D. Koven, A. Ahlström, M. A. Arain, I. Baker, J. M. Chen, P. Ciais, C. Davidson, M. Dietze, B. El-Masri, D. Hayes, C. Huntingford, A. K. Jain, P. E. Levy, M. R. Lomas, B. Poulter, D. Price, A. K. Sahoo, K. Schaefer, H. Tian, E. Tomelleri, H. Verbeeck, N. Viovy, R. Wania, N. Zeng, and C. E. Miller
Biogeosciences, 11, 4271–4288, https://doi.org/10.5194/bg-11-4271-2014, https://doi.org/10.5194/bg-11-4271-2014, 2014
D. N. Huntzinger, C. Schwalm, A. M. Michalak, K. Schaefer, A. W. King, Y. Wei, A. Jacobson, S. Liu, R. B. Cook, W. M. Post, G. Berthier, D. Hayes, M. Huang, A. Ito, H. Lei, C. Lu, J. Mao, C. H. Peng, S. Peng, B. Poulter, D. Riccuito, X. Shi, H. Tian, W. Wang, N. Zeng, F. Zhao, and Q. Zhu
Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, https://doi.org/10.5194/gmd-6-2121-2013, 2013
C. Le Quéré, R. J. Andres, T. Boden, T. Conway, R. A. Houghton, J. I. House, G. Marland, G. P. Peters, G. R. van der Werf, A. Ahlström, R. M. Andrew, L. Bopp, J. G. Canadell, P. Ciais, S. C. Doney, C. Enright, P. Friedlingstein, C. Huntingford, A. K. Jain, C. Jourdain, E. Kato, R. F. Keeling, K. Klein Goldewijk, S. Levis, P. Levy, M. Lomas, B. Poulter, M. R. Raupach, J. Schwinger, S. Sitch, B. D. Stocker, N. Viovy, S. Zaehle, and N. Zeng
Earth Syst. Sci. Data, 5, 165–185, https://doi.org/10.5194/essd-5-165-2013, https://doi.org/10.5194/essd-5-165-2013, 2013
W. Zheng, B. Wu, J. He, and Y. Yu
Clim. Past, 9, 453–466, https://doi.org/10.5194/cp-9-453-2013, https://doi.org/10.5194/cp-9-453-2013, 2013
Related subject area
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Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Coupling the urban canopy model TEB (SURFEXv9.0) with the radiation model SPARTACUS-Urbanv0.6.1 for more realistic urban radiative exchange calculation
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
A new set of indicators for model evaluation complementing to FAIRMODE’s MQO
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
The third Met Office Unified Model-JULES Regional Atmosphere and Land Configuration, RAL3
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
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Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
UA-ICON with NWP physics package (version: ua-icon-2.1): mean state and variability of the middle atmosphere
Assessment of object-based indices to identify convective organization
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The Global Forest Fire Emissions Prediction System version 1.0
Impact of Multiple Radar Wind Profilers Data Assimilation on Convective Scale Short-Term Rainfall Forecasts: OSSE Studies over the Beijing-Tianjin-Hebei region
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
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The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
EGUsphere, https://doi.org/10.5194/egusphere-2024-3690, https://doi.org/10.5194/egusphere-2024-3690, 2025
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We assess the relevance and utility indicators developed within FAIRMODE by evaluating 9 CAMS models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and Winter-Summer gradients reveal issues. O3 evaluation shows that e.g. seasonal gradients are useful. Overall, the indicators provide valuable insights into model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
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., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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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 the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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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.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Revised manuscript accepted for GMD
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Wonbae Bang, Jacob Carlin, Kwonil Kim, Alexander Ryzhkov, Guosheng Liu, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-179, https://doi.org/10.5194/gmd-2024-179, 2024
Revised manuscript accepted for GMD
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Microphysics model-based diagnosis such as the spectral bin model (SBM) recently has been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM have relatively higher accuracy about snow and wetsnow events whereas lower accuracy about rain event. When microphysics scheme in the SBM was optimized for the corresponding region, accuracy about rain events was improved.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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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.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-194, https://doi.org/10.5194/gmd-2024-194, 2024
Revised manuscript accepted for GMD
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A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing-Tianjin-Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
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Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and...
