Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-3037-2021
© Author(s) 2021. 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-14-3037-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
27 May 2021
Development and technical paper |
| 27 May 2021
| 27 May 2021
Development and evaluation of CO2 transport in MPAS-A v6.3
Department of Geography and Environmental Studies, Central Michigan University, Mount Pleasant, MI, USA
Institute for Great Lakes Research, Central Michigan University, Mount Pleasant, MI, USA
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USA
now at: Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA
Kenneth J. Davis
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, USA
Sandip Pal
Department of Geosciences, Texas Tech University, Lubbock, TX, USA
Josep-Anton Morguí
Environmental Science and Technology Institute, Universitat Autònoma de Barcelona, ICTA-UAB, Bellaterra, Spain
Department of Evolutionary Biology, Ecology and Environmental Sciences, Universitat de Barcelona, BEECA-UB, Barcelona, Spain
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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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Xueying Yu, Dylan B. Millet, Kelley C. Wells, Daven K. Henze, Hansen Cao, Timothy J. Griffis, Eric A. Kort, Genevieve Plant, Malte J. Deventer, Randall K. Kolka, D. Tyler Roman, Kenneth J. Davis, Ankur R. Desai, Bianca C. Baier, Kathryn McKain, Alan C. Czarnetzki, and A. Anthony Bloom
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Petter Weibring, Dirk Richter, James G. Walega, Alan Fried, Joshua DiGangi, Hannah Halliday, Yonghoon Choi, Bianca Baier, Colm Sweeney, Ben Miller, Kenneth J. Davis, Zachary Barkley, and Michael D. Obland
Atmos. Meas. Tech., 13, 6095–6112, https://doi.org/10.5194/amt-13-6095-2020, https://doi.org/10.5194/amt-13-6095-2020, 2020
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Claudia Grossi, Scott D. Chambers, Olivier Llido, Felix R. Vogel, Victor Kazan, Alessandro Capuana, Sylvester Werczynski, Roger Curcoll, Marc Delmotte, Arturo Vargas, Josep-Anton Morguí, Ingeborg Levin, and Michel Ramonet
Atmos. Meas. Tech., 13, 2241–2255, https://doi.org/10.5194/amt-13-2241-2020, https://doi.org/10.5194/amt-13-2241-2020, 2020
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The sustainable support of radon metrology at the environmental level offers new scientific possibilities for the quantification of greenhouse gas (GHG) emissions and the determination of their source terms as well as for the identification of radioactive sources for the assessment of radiation exposure. This study helps to harmonize the techniques commonly used for atmospheric radon and radon progeny activity concentration measurements.
Nikolay V. Balashov, Kenneth J. Davis, Natasha L. Miles, Thomas Lauvaux, Scott J. Richardson, Zachary R. Barkley, and Timothy A. Bonin
Atmos. Chem. Phys., 20, 4545–4559, https://doi.org/10.5194/acp-20-4545-2020, https://doi.org/10.5194/acp-20-4545-2020, 2020
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An accurate independent verification methodology to estimate methane (a powerful greenhouse gas) emissions is essential for the effective implementation of policies that aim to reduce the impacts of climate change. In this paper, four uncertainties that complicate the independent estimation of urban methane emissions are identified: the definition of urban domain, background heterogeneity, emissions temporal variability, and missing sources. Ways to improve emission estimates are suggested.
Anna Agustí-Panareda, Michail Diamantakis, Sébastien Massart, Frédéric Chevallier, Joaquín Muñoz-Sabater, Jérôme Barré, Roger Curcoll, Richard Engelen, Bavo Langerock, Rachel M. Law, Zoë Loh, Josep Anton Morguí, Mark Parrington, Vincent-Henri Peuch, Michel Ramonet, Coleen Roehl, Alex T. Vermeulen, Thorsten Warneke, and Debra Wunch
Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019, https://doi.org/10.5194/acp-19-7347-2019, 2019
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Liza I. Díaz-Isaac, Thomas Lauvaux, Marc Bocquet, and Kenneth J. Davis
Atmos. Chem. Phys., 19, 5695–5718, https://doi.org/10.5194/acp-19-5695-2019, https://doi.org/10.5194/acp-19-5695-2019, 2019
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We demonstrate that transport model errors, one of the main contributors to the uncertainty in regional CO2 inversions, can be represented by a small-size ensemble carefully calibrated with meteorological data. Our results also confirm transport model errors represent a significant fraction of the model–data mismatch in CO2 mole fractions and hence in regional inverse CO2 fluxes.
Julian Kostinek, Anke Roiger, Kenneth J. Davis, Colm Sweeney, Joshua P. DiGangi, Yonghoon Choi, Bianca Baier, Frank Hase, Jochen Groß, Maximilian Eckl, Theresa Klausner, and André Butz
Atmos. Meas. Tech., 12, 1767–1783, https://doi.org/10.5194/amt-12-1767-2019, https://doi.org/10.5194/amt-12-1767-2019, 2019
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We demonstrate the successful adaption of a laser-based spectrometer for airborne in situ trace gas measurements. The modified instrument allows for precise and simultaneous airborne observation of five climatologically relevant gases. We further report on instrument performance during a first field deployment over the eastern and central USA.
Martha P. Butler, Thomas Lauvaux, Sha Feng, Junjie Liu, Kevin W. Bowman, and Kenneth J. Davis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-342, https://doi.org/10.5194/gmd-2018-342, 2019
Revised manuscript not accepted
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This paper describes a mass-conserving framework for computing time-varying lateral boundary conditions from global model carbon dioxide concentrations for introduction into the WRF-Chem regional model. The goal is to create a laboratory environment in which carbon dioxide transport uncertainties may be explored separately from inversion-derived flux uncertainties. The software is currently available on GitHub at https://github.com/psu-inversion/WRF_Boundary_Coupling.
Liza I. Díaz-Isaac, Thomas Lauvaux, and Kenneth J. Davis
Atmos. Chem. Phys., 18, 14813–14835, https://doi.org/10.5194/acp-18-14813-2018, https://doi.org/10.5194/acp-18-14813-2018, 2018
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Atmospheric inversions rely on the accurate representation of the atmospheric dynamics in order to produce reliable surface fluxes. In this work, we evaluate the sensitivity of a state-of-the-art mesoscale atmospheric model to the different physics parameterizations and forcing. We conclude that no model configuration is optimal across an entire region. Therefore, we recommend an ensemble approach or the assimilation of meteorological observations in future inversion studies.
Tao Zheng, Nancy H. F. French, and Martin Baxter
Geosci. Model Dev., 11, 1725–1752, https://doi.org/10.5194/gmd-11-1725-2018, https://doi.org/10.5194/gmd-11-1725-2018, 2018
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We developed WRF-CO2 4D-Var, a carbon dioxide data assimilation system based on the online atmospheric chemistry–transport model WRF-Chem. The accuracy of the model for sensitivity calculation and inverse modeling is assessed with pseudo-observation data. In this system, carbon dioxide is treated as an atmospheric tracer and its influence on meteorology is ignored. This system provides a useful model tool for regional-scale carbon source attribution and uncertainty assessment.
Claudia Grossi, Felix R. Vogel, Roger Curcoll, Alba Àgueda, Arturo Vargas, Xavier Rodó, and Josep-Anton Morguí
Atmos. Chem. Phys., 18, 5847–5860, https://doi.org/10.5194/acp-18-5847-2018, https://doi.org/10.5194/acp-18-5847-2018, 2018
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To gain a full picture of the Spanish (and European) GHG balance, understanding of CH4 emissions in different regions is a critical challenge, as is the improvement of bottom-up inventories for all European regions. This study uses, among other elements, GHG, meteorological and 222Rn tracer data from a Spanish region to understand the main causes of temporal variability of GHG mixing ratios. The study can offer new insights into regional emissions by identifying the impacts of changing sources.
Natasha L. Miles, Douglas K. Martins, Scott J. Richardson, Christopher W. Rella, Caleb Arata, Thomas Lauvaux, Kenneth J. Davis, Zachary R. Barkley, Kathryn McKain, and Colm Sweeney
Atmos. Meas. Tech., 11, 1273–1295, https://doi.org/10.5194/amt-11-1273-2018, https://doi.org/10.5194/amt-11-1273-2018, 2018
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Analyzers measuring methane and methane isotopic ratio were deployed at four towers in the Marcellus Shale natural gas extraction region of Pennsylvania. The methane isotopic ratio is helpful for differentiating emissions from natural gas activities from other sources (e.g., landfills). We describe the analyzer calibration. The signals observed in the study region were generally small, but the instrumental performance demonstrated here could be used in regions with stronger enhancements.
Xinxin Ye, Thomas Lauvaux, Eric A. Kort, Tomohiro Oda, Sha Feng, John C. Lin, Emily Yang, and Dien Wu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-1022, https://doi.org/10.5194/acp-2017-1022, 2017
Revised manuscript not accepted
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Rapid global urbanization and significant fossil fuel consumption by cities emphasize the necessity of achieving independent and accurate quantification of the carbon emissions from urban areas. In this paper, we assess the potential of using total column CO2 concentration observed from satellite to quantify fossil-fuel carbon emissions from cities. This study could give insights into the capability of satellite observations on monitoring of the emissions on local scale.
Zachary R. Barkley, Thomas Lauvaux, Kenneth J. Davis, Aijun Deng, Natasha L. Miles, Scott J. Richardson, Yanni Cao, Colm Sweeney, Anna Karion, MacKenzie Smith, Eric A. Kort, Stefan Schwietzke, Thomas Murphy, Guido Cervone, Douglas Martins, and Joannes D. Maasakkers
Atmos. Chem. Phys., 17, 13941–13966, https://doi.org/10.5194/acp-17-13941-2017, https://doi.org/10.5194/acp-17-13941-2017, 2017
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This study quantifies methane emissions from natural gas production in north-eastern Pennsylvania. Methane observations from 10 flights in spring 2015 are compared to model-projected values, and methane emissions from natural gas are adjusted within the model to create the best match between the two data sets. This study find methane emissions from natural gas production to be low and may be indicative of characteristics of the basin that make sources from north-eastern Pennsylvania unique.
Sha Feng, Thomas Lauvaux, Sally Newman, Preeti Rao, Ravan Ahmadov, Aijun Deng, Liza I. Díaz-Isaac, Riley M. Duren, Marc L. Fischer, Christoph Gerbig, Kevin R. Gurney, Jianhua Huang, Seongeun Jeong, Zhijin Li, Charles E. Miller, Darragh O'Keeffe, Risa Patarasuk, Stanley P. Sander, Yang Song, Kam W. Wong, and Yuk L. Yung
Atmos. Chem. Phys., 16, 9019–9045, https://doi.org/10.5194/acp-16-9019-2016, https://doi.org/10.5194/acp-16-9019-2016, 2016
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We developed a high-resolution land–atmosphere modelling system for urban CO2 emissions over the LA Basin. We evaluated various model configurations, FFCO2 products, and the impact of the model resolution. FFCO2 emissions outpace the atmospheric model resolution to represent the CO2 concentration variability across the basin. A novel forward model approach is presented to evaluate the surface measurement network, reinforcing the importance of using high-resolution emission products.
Sally Newman, Xiaomei Xu, Kevin R. Gurney, Ying Kuang Hsu, King Fai Li, Xun Jiang, Ralph Keeling, Sha Feng, Darragh O'Keefe, Risa Patarasuk, Kam Weng Wong, Preeti Rao, Marc L. Fischer, and Yuk L. Yung
Atmos. Chem. Phys., 16, 3843–3863, https://doi.org/10.5194/acp-16-3843-2016, https://doi.org/10.5194/acp-16-3843-2016, 2016
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Combining 14C and 13C data from the Los Angeles, CA megacity with background data allows source attribution of CO2 emissions among biosphere, natural gas, and gasoline. The 8-year record of CO2 emissions from fossil fuel burning is consistent with "The Great Recession" of 2008–2010. The long-term trend and source attribution are consistent with government inventories. Seasonal patterns agree with the high-resolution Hestia-LA emission data product, when seasonal wind directions are considered.
Susan L. Brantley, Roman A. DiBiase, Tess A. Russo, Yuning Shi, Henry Lin, Kenneth J. Davis, Margot Kaye, Lillian Hill, Jason Kaye, David M. Eissenstat, Beth Hoagland, Ashlee L. Dere, Andrew L. Neal, Kristen M. Brubaker, and Dan K. Arthur
Earth Surf. Dynam., 4, 211–235, https://doi.org/10.5194/esurf-4-211-2016, https://doi.org/10.5194/esurf-4-211-2016, 2016
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In order to better understand and forecast the evolution of the environment from the top of the vegetation canopy down to bedrock, numerous types of intensive measurements have been made over several years in a small watershed. The ability to expand such a study to larger areas and different environments requiring fewer measurements is essential. This study presents one possible approach to such an expansion, to collect necessary and sufficient measurements in order to forecast this evolution.
L. Haszpra, Z. Barcza, T. Haszpra, Zs. Pátkai, and K. J. Davis
Atmos. Meas. Tech., 8, 1657–1671, https://doi.org/10.5194/amt-8-1657-2015, https://doi.org/10.5194/amt-8-1657-2015, 2015
A. W. King, R. J. Andres, K. J. Davis, M. Hafer, D. J. Hayes, D. N. Huntzinger, B. de Jong, W. A. Kurz, A. D. McGuire, R. Vargas, Y. Wei, T. O. West, and C. W. Woodall
Biogeosciences, 12, 399–414, https://doi.org/10.5194/bg-12-399-2015, https://doi.org/10.5194/bg-12-399-2015, 2015
M. O. L. Cambaliza, P. B. Shepson, D. R. Caulton, B. Stirm, D. Samarov, K. R. Gurney, J. Turnbull, K. J. Davis, A. Possolo, A. Karion, C. Sweeney, B. Moser, A. Hendricks, T. Lauvaux, K. Mays, J. Whetstone, J. Huang, I. Razlivanov, N. L. Miles, and S. J. Richardson
Atmos. Chem. Phys., 14, 9029–9050, https://doi.org/10.5194/acp-14-9029-2014, https://doi.org/10.5194/acp-14-9029-2014, 2014
T. W. Hilton, K. J. Davis, and K. Keller
Biogeosciences, 11, 217–235, https://doi.org/10.5194/bg-11-217-2014, https://doi.org/10.5194/bg-11-217-2014, 2014
A. Font, C. S. B. Grimmond, J.-A. Morguí, S. Kotthaus, M. Priestman, and B. Barratt
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-13465-2013, https://doi.org/10.5194/acpd-13-13465-2013, 2013
Revised manuscript not accepted
Related subject area
Atmospheric sciences
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)
Carbon dioxide plume dispersion simulated at hectometer scale using DALES: model formulation and observational evaluation
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
Atmospheric moisture tracking with WAM2layers v3
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
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
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
Diagnosis of winter precipitation types using Spectral Bin Model (SBM): Comparison of five methods using ICE-POP 2018 field experiment data
The Global Forest Fire Emissions Prediction System 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.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
EGUsphere, https://doi.org/10.5194/egusphere-2024-3721, https://doi.org/10.5194/egusphere-2024-3721, 2024
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We introduce a new simulation platform based on the Dutch Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in the turbulent environments with hectometer resolution. This model incorporates both anthropogenic emission inventory and ecosystem exchanges. Simulation results for the main urban area in the Netherlands demonstrate a strong potential of DALES to enhance CO2 emission modeling, which is important for refining their reduction strategies.
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.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
EGUsphere, https://doi.org/10.5194/egusphere-2024-3401, https://doi.org/10.5194/egusphere-2024-3401, 2024
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We introduce a new version of WAM2layers, a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data became a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent and reliable, and easier to maintain.
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.
Cited articles
Agustí-Panareda, A., Massart, S., Chevallier, F., Boussetta, S., Balsamo, G., Beljaars, A., Ciais, P., Deutscher, N. M., Engelen, R., Jones, L., Kivi, R., Paris, J.-D., Peuch, V.-H., Sherlock, V., Vermeulen, A. T., Wennberg, P. O., and Wunch, D.: Forecasting global atmospheric CO2, Atmos. Chem. Phys., 14, 11959–11983, https://doi.org/10.5194/acp-14-11959-2014, 2014. a, b, c
Agusti-Panareda, A., Diamantakis, M., Bayona, V., Klappenbach, F., and Butz, A.: Improving the inter-hemispheric gradient of total column atmospheric CO2 and CH4 in simulations with the ECMWF semi-Lagrangian atmospheric global model, Geosci. Model Dev., 10, 1–18, https://doi.org/10.5194/gmd-10-1-2017, 2017. a
Agustí-Panareda, A., Diamantakis, M., Massart, S., Chevallier, F., Muñoz-Sabater, J., Barré, J., Curcoll, R., Engelen, R., Langerock, B., Law, R. M., Loh, Z., Morguí, J. A., Parrington, M., Peuch, V.-H., Ramonet, M., Roehl, C., Vermeulen, A. T., Warneke, T., and Wunch, D.: Modelling CO2 weather – why horizontal resolution matters, Atmos. Chem. Phys., 19, 7347–7376, https://doi.org/10.5194/acp-19-7347-2019, 2019. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Andrews, A. E., Kofler, J. D., Trudeau, M. E., Williams, J. C., Neff, D. H., Masarie, K. A., Chao, D. Y., Kitzis, D. R., Novelli, P. C., Zhao, C. L., Dlugokencky, E. J., Lang, P. M., Crotwell, M. J., Fischer, M. L., Parker, M. J., Lee, J. T., Baumann, D. D., Desai, A. R., Stanier, C. O., De Wekker, S. F. J., Wolfe, D. E., Munger, J. W., and Tans, P. P.: CO2, CO, and CH4 measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts, Atmos. Meas. Tech., 7, 647–687, https://doi.org/10.5194/amt-7-647-2014, 2014. a, b, c, d, e, f, g
Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data assimilation
for atmospheric CO2, Tellus B, 58, 359–365, 2006. a
Blumenstock, T., Hase, F., Schneider, M., García, O. E., and Sepúlveda, E.:
TCCON data from Izana (ES), Release GGG2014.R1,
https://doi.org/10.14291/TCCON.GGG2014.IZANA01.R1, 2017. a
Borge, R., Alexandrov, V., del Vas, J. J., Lumbreras, J., and Rodriguez, E.: A
comprehensive sensitivity analysis of the WRF model for air quality
applications over the Iberian Peninsula, Atmos. Environ., 42,
8560–8574, https://doi.org/10.1016/j.atmosenv.2008.08.032, 2008. a
Brunke, E., Labuschagne, C., Parker, B., Scheel, H., and Whittlestone, S.:
Baseline air mass selection at Cape Point, South Africa: application of
Rn-222 and other filter criteria to CO2, Atmos. Environ., 38,
5693–5702, https://doi.org/10.1016/j.atmosenv.2004.04.024, 2004. a
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model
with the Penn State-NCAR MM5 modeling system. Part I: Model implementation
and sensitivity, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001. a, b
Conway, T. J. and Thoning, K. W.: Short-term variations of atmospheric carbon
dioxide at the South Pole, Anarctic J., 25, 236–238, 1990. a
Davies, T.: Lateral boundary conditions for limited area models, Q.
J. Roy. Meteor. Soc., 140, 185–196,
https://doi.org/10.1002/qj.2127, 2014. a
Davis, K., Baier, B., Z., B., Bowman, K., Boyer, A., and Browell, E.:
Atmospheric Carbon and Transport (ACT) – America: A multi‐year airborne
mission to study fluxes and transport of CO2 and CH4 across the eastern
United States, American Geophysical Union Fall Meeting, San Francisco, CA, USA, 2018a. a
Davis, K. J., Obland, M. D., Lin, B., Lauvaux, T., O'Dell, C., Meadows, B., Browell, E. V., DiGangi, J. P., Sweeney, C., McGill, M. J., Barrick, J. D., Nehrir, A. R., Yang, M. M., Bennett, J. R., Baier, B. C., Roiger, A., Pal, S., Gerken, T., Fried, A., Feng, S., Shrestha, R., Shook, M. A., Chen, G., Campbell, L. J., Barkley, Z. R., and Pauly, R. M.: ACT–America: L3 Merged In Situ Atmospheric Trace Gases and Flask Data, Eastern USA [Data set], ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1593, 2018b. a, b
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Koehler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park,
B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteor.
Soc.,, 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b
De Mazière, M., Sha, M. K., Desmet, F., Hermans, C., Scolas, F., Kumps, N.,
Metzger, J.-M., Duflot, V., and Cammas, J.-P.: TCCON data from Réunion
Island (RE), Release GGG2014.R0,
https://doi.org/10.14291/TCCON.GGG2014.REUNION01.R0/ 1149288, 2014. a
Deutscher, N. M., Notholt, J., Messerschmidt, J., Weinzierl, C., Warneke, T.,
Petri, C., and Grupe, P.: TCCON data from Bialystok (PL), Release GGG2014.R1,
https://doi.org/10.14291/TCCON.GGG2014.BIALYSTOK01.R1/ 1183984, 2015. a
Diamantakis, M. and Flemming, J.: Global mass fixer algorithms for conservative tracer transport in the ECMWF model, Geosci. Model Dev., 7, 965–979, https://doi.org/10.5194/gmd-7-965-2014, 2014. a, b
Díaz-Isaac, L. I., Lauvaux, T., and Davis, K. J.: Impact of physical parameterizations and initial conditions on simulated atmospheric transport and CO2 mole fractions in the US Midwest, Atmos. Chem. Phys., 18, 14813–14835, https://doi.org/10.5194/acp-18-14813-2018, 2018. a
Díaz-Isaac, L. I., Lauvaux, T., Bocquet, M., and Davis, K. J.: Calibration of a multi-physics ensemble for estimating the uncertainty of a greenhouse gas atmospheric transport model, Atmos. Chem. Phys., 19, 5695–5718, https://doi.org/10.5194/acp-19-5695-2019, 2019. a
Feist, D. G., Arnold, S. G., John, N., and Geibel, M. C.: TCCON data from
Ascension Island, Saint Helena, Ascension and Tristan da Cunha, Release
GGG2014R0, TCCON data archive, hosted by the Carbon Dioxide Information
Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA,
https://doi.org/10.14291/tccon.ggg2014.ascension01.R0/1149285, 2014. a
Feng, S., Lauvaux, T., Barkley, Z. R., Butler, M. B., Deng, A.,
Gaudet, B., and Davis, K. J.: Full WRF-Chem output in support of the NASA
Atmospheric Carbon and Transport (ACT)-America project (7/1/2016 –
7/31/2019). The Pennsylvania State University Data Commons, University Park,
Pennsylvania, USA, https://doi.org/10.26208/49kd-b637, 2020. a
Feng, S., Lauvaux, T., Newman, S., Rao, P., Ahmadov, R., Deng, A., Díaz-Isaac, L. I., Duren, R. M., Fischer, M. L., Gerbig, C., Gurney, K. R., Huang, J., Jeong, S., Li, Z., Miller, C. E., O'Keeffe, D., Patarasuk, R., Sander, S. P., Song, Y., Wong, K. W., and Yung, Y. L.: Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO2 emissions, Atmos. Chem. Phys., 16, 9019–9045, https://doi.org/10.5194/acp-16-9019-2016, 2016. a, b
Feng, S., Lauvaux, T., Davis, K. J., Keller, K., Zhou, Y., Williams, C., Schuh,
A. E., Liu, J., and Baker, I.: Seasonal Characteristics of Model
Uncertainties From Biogenic Fluxes, Transport, and Large-Scale Boundary
Inflow in Atmospheric CO2 Simulations Over North America, J.
Geophys. Res.-Atmos., 124, 14325–14346,
https://doi.org/10.1029/2019JD031165, 2019. a, b
Francey, R. J., Steele, L. P., Spencer, D. A., Langenfelds, R. L., Law, R. M.,
Krummel, P. B., Fraser, P. J., Etheridge, D. M., Derek, N., Coram, S. A.,
Cooper, L. N., Allison, C. E., Porter, L., and Baly, S.: The CSIRO
(Australia) measurement of greenhouse gases in the global atmosphere, report
of the 11th WMO/IAEA Meeting of Experts on Carbon Dioxide Concentration and
Related Tracer Measurement Techniques, Tokyo, Japan, September 2001, edited
by: Toru, S. and Kazuto, S., World Meteorological Organization Global
Atmosphere Watch, Geneva, Switzerland, 2003. a
Fritsch, J. M. and Chappell, C. F.: Numerical prediction of convectively
driven mesoscale pressure systems. Part I: convective parameterization,
J. Atmos. Sci., 37, 1722–1733,
https://doi.org/10.1175/1520-0469(1980)037<1722:NPOCDM>2.0.CO;2, 1980. a
Gaudry, A., Monfray, P., Polian, G., Bonsang, G., Ardouin, B., Jegou, A., and
Lambert, G.: Nonseasonnal variations of atmospheric CO2 concentrations at
Amsterdam Island, Tellus B, 43,
136–143, https://doi.org/10.1034/j.1600-0889.1991.00008.x, 1991. a
Gerbig, C., Körner, S., and Lin, J. C.: Vertical mixing in atmospheric tracer transport models: error characterization and propagation, Atmos. Chem. Phys., 8, 591–602, https://doi.org/10.5194/acp-8-591-2008, 2008. a
Gerbig, C., Dolman, A. J., and Heimann, M.: On observational and modelling strategies targeted at regional carbon exchange over continents, Biogeosciences, 6, 1949–1959, https://doi.org/10.5194/bg-6-1949-2009, 2009. a
Gockede, M., Turner, D. P., Michalak, A. M., Vickers, D., and Law, B. E.:
Sensitivity of a subregional scale atmospheric inverse CO2 modeling
framework to boundary conditions, J. Geophys. Res., 115, D24112,
https://doi.org/10.1029/2010JD014443, 2010. a
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G.,
Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A.,
Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M.,
Hannay, 15 C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N.,
Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H.,
Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B.,
Price, S. F., Qian, Y., Rasch, P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler,
E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A.
K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S.,
Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y.,
Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and
Evaluation at Standard Resolution, J. Adv. Model. Earth
Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a
Gomez-Pelaez, A. J. and Ramos, R.: Improvements in the Carbon Dioxide and
Methane Continuous Measurement Programs at Izana Global GAW Station (Spain)
during 2007–2009, in: GAW report (No. 194) of the 15th WMO/IAEA Meeting of
Experts on Carbon Dioxide, Other Greenhouse Gases, and Related Tracer
Measurement Techniques, Jena, Germany; 7–10 September 2009, edited by:
Brand, W. A., World Meteorological Organization, TD No. 1553, 2005. a
Grell, G., Freitas, S. R., Stuefer, M., and Fast, J.: Inclusion of biomass burning in WRF-Chem: impact of wildfires on weather forecasts, Atmos. Chem. Phys., 11, 5289–5303, https://doi.org/10.5194/acp-11-5289-2011, 2011. a
Griffith, D. W., Deutscher, N. M., Velazco, V. A., Wennberg, P. O., Yavin, Y.,
Keppel-Aleks, G., Washenfelder, R. A., Toon, G. C., Blavier, J.-F.,
Paton-Walsh, C., Jones, N. B., Kettlewell, G. C., Connor, B. J., Macatangay,
R. C., Roehl, C., Ryczek, M., Glowacki, J., Culgan, T., and Bryant, G. W.:
TCCON data from Darwin (AU), Release GGG2014.R0,
https://doi.org/10.14291/TCCON.GGG2014.DARWIN01.R0/ 1149290, 2014a. a
Griffith, D. W., Velazco, V. A., Deutscher, N. M., Paton-Walsh, C., Jones,
N. B., Wilson, S. R., Macatangay, R. C., Kettlewell, G. C., Buchholz, R. R.,
and Riggenbach, M. O.: TCCON data from Wollongong (AU), Release GGG2014.R0,
https://doi.org/10.14291/TCCON.GGG2014.WOLLONGONG01.R0/ 1149291, 2014b. a
Halter, B., Harris, J., and Conway, T.: Component signals in the record of
atmospheric carbon dioxide concentration at American Samoa, J.
Geophys. Res.-Atmos., 93, 15914–15918,
https://doi.org/10.1029/JD093iD12p15914, 1988. a
Hase, F., Blumenstock, T., Dohe, S., Groß, J., and Kiel, M.: TCCON data from
Karlsruhe (DE), Release GGG2014.R1,
https://doi.org/10.14291/TCCON.GGG2014.KARLSRUHE01. R1/1182416, 2015. a
Haszpra, L., Barcza, Z., Bakwin, P., Berger, B., Davis, K., and Weidinger, T.:
Measuring system for the long-term monitoring of biosphere/atmosphere
exchange of carbon dioxide, J. Geophys. Res.-Atmos.,
106, 3057–3069, https://doi.org/10.1029/2000JD900600, 2001. a
Hatakka, J., Aalto, T., Aaltonen, V., Aurela, M., Hakola, H., Komppula, M.,
Laurila, T., Lihavainen, H., Paatero, J., Salminen, K., and Viisanen, Y.:
Overview of the atmospheric research activities and results at Pallas GAW
station, Boreal Environ. Res., 8, 365–383, 2003. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, A.,
Munoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Holm, E., Janiskova, M., Keeley,
S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I.,
Vamborg, F., Villaume, S., and Thepaut, J.-N.: The ERA5 global reanalysis,
Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020. a
Hong, S., Dudhia, J., and Chen, S.: A revised approach to ice microphysical
processes for the bulk parameterization of clouds and precipitation,
Mon. Weather Rev., 132, 103–120,
https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2, 2004. a
Hu, L., Andrews, A. E., Thoning, K. W., Sweeney, C., Miller, J. B., Michalak,
A. M., Dlugokencky, E., Tans, P. P., Shiga, Y. P., Mountain, M., Nehrkorn,
T., Montzka, S. A., McKain, K., Kofler, J., Trudeau, M., Michel, S. E.,
Biraud, S. C., Fischer, M. L., Worthy, D. E. J., Vaughn, B. H., White, J.
W. C., Yadav, V., Basu, S., and van der Velde, I. R.: Enhanced North American
carbon uptake associated with El Niño, Sci. Adv., 5, eaaw0076,
https://doi.org/10.1126/sciadv.aaw0076, 2019. a
Hu, X.-M., Nielsen-Gammon, J. W., and Zhang, F.: Evaluation of Three Planetary
Boundary Layer Schemes in the WRF Model, J. Appl. Meteorol.
Climatol., 49, 1831–1844, 2010. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A.,
and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys.
Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a, b, c
Iraci, L. T., Podolske, J. R., Hillyard, P. W., Roehl, C., Wennberg, P. O.,
Blavier, J.-F., Landeros, J., Allen, N., Wunch, D., Zavaleta, J., Quigley,
E., Osterman, G. B., Albertson, R., Dunwoody, K., and Boyden, H.: TCCON data
from Edwards (US), Release GGG2014.R1,
https://doi.org/10.14291/TCCON.GGG2014.EDWARDS01.R1/ 1255068, 2016. a
Jacobson, A. R., Fletcher, S. E. M., Gruber, N., Sarmiento, J. L., and Gloor,
M.: A joint atmosphere-ocean inversion for surface fluxes of carbon dioxide:
1. Methods and global-scale fluxes, Global Biogeochem. Cycles, 21, GB1020,
https://doi.org/10.1029/2006GB002703, 2007. a
Jacobson, A. R., Schuldt, K. N., Miller, J. B., Oda, T., Tans, P., Arlyn
Andrews, Mund, J., Ott, L., Collatz, G. J., Aalto, T., Afshar, S., Aikin,
K., Aoki, S., Apadula, F., Baier, B., Bergamaschi, P., Beyersdorf, A.,
Biraud, S. C., Bollenbacher, A., Bowling, D., Brailsford, G., Abshire, J. B.,
Chen, G., Huilin Chen, Lukasz Chmura, Sites Climadat, Colomb, A.,
Conil, S., Cox, A., Cristofanelli, P., Cuevas, E., Curcoll, R., Sloop, C. D.,
Davis, K., Wekker, S. D., Delmotte, M., DiGangi, J. P., Dlugokencky, E.,
Ehleringer, J., Elkins, J. W., Emmenegger, L., Fischer, M. L., Forster, G.,
Frumau, A., Galkowski, M., Gatti, L. V., Gloor, E., Griffis, T., Hammer, S.,
Haszpra, L., Hatakka, J., Heliasz, M., Hensen, A., Hermanssen, O., Hintsa,
E., Holst, J., Jaffe, D., Karion, A., Kawa, S. R., Keeling, R., Keronen, P.,
Kolari, P., Kominkova, K., Kort, E., Krummel, P., Kubistin, D., Labuschagne,
C., Langenfelds, R., Laurent, O., Laurila, T., Lauvaux, T., Law, B., Lee, J.,
Lehner, I., Leuenberger, M., Levin, I., Levula, J., Lin, J., Lindauer, M.,
Loh, Z., Lopez, M., Myhre, C. L., Machida, T., Mammarella, I., Manca, G.,
Manning, A., Manning, A., Marek, M. V., Marklund, P., Martin, M. Y.,
Matsueda, H., McKain, K., Meijer, H., Meinhardt, F., Miles, N., Miller,
C. E., Mölder, M., Montzka, S., Moore, F., Josep-Anton Morgui, Morimoto,
S., Munger, B., Jaroslaw Necki, Newman, S., Nichol, S., Niwa, Y.,
O'Doherty, S., Mikaell Ottosson-Löfvenius, Paplawsky, B., Peischl, J.,
Peltola, O., Jean-Marc Pichon, Piper, S., Plass-Dölmer, C., Ramonet, M.,
Reyes-Sanchez, E., Richardson, S., Riris, H., Ryerson, T., Saito, K.,
Sargent, M., Sasakawa, M., Sawa, Y., Say, D., Scheeren, B., Schmidt, M.,
Schmidt, A., Schumacher, M., Shepson, P., Shook, M., Stanley, K.,
Steinbacher, M., Stephens, B., Sweeney, C., Thoning, K., Torn, M., Turnbull,
J., Tørseth, K., Bulk, P. V. D., Laan-Luijkx, I. T. V. D., Dinther, D. V.,
Vermeulen, A., Viner, B., Vitkova, G., Walker, S., Weyrauch, D., Wofsy, S.,
Worthy, D., Dickon Young, and Miroslaw Zimnoch: CarbonTracker CT2019, https://doi.org/10.25925/39M3-6069, 2020. a, b, c, d, e, f, g, h, i
Kain, J. S. and Fritsch, J. M.: A one-dimensional entraining detraining plume
model and its application in convective parameterization, J.
Atmos. Sci., 47, 2784–2802,
https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990. a
Kawakami, S., Ohyama, H., Arai, K., Okumura, H., Taura, C., Fukamachi, T., and
Sakashita, M.: TCCON data from Saga (JP), Release GGG2014.R0,
https://doi.org/10.14291/TCCON.GGG2014.SAGA01.R0/1149 283, 2014. a
Kivi, R., Heikkinen, P., and Kyro: TCCON data from Sodankyla, Finland, Release
GGG2014R0., TCCON data archive, hosted by the Carbon Dioxide Information
Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA,
https://doi.org/10.14291/tccon.ggg2014.sodankyla01.R0/1149280, 2014. a
Kretschmer, R., Gerbig, C., Karstens, U., and Koch, F.-T.: Error characterization of CO2 vertical mixing in the atmospheric transport model WRF-VPRM, Atmos. Chem. Phys., 12, 2441–2458, https://doi.org/10.5194/acp-12-2441-2012, 2012. a
Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van Velthoven, P., Peters, W., Dentener, F., and Bergamaschi, P.: The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417–432, https://doi.org/10.5194/acp-5-417-2005, 2005. a, b
Lauvaux, T. and Davis, K. J.: Planetary boundary layer errors in mesoscale
inversions of column-integrated CO2 measurements, J. Geophys.
Res.-Atmos., 119, 490–508, https://doi.org/10.1002/2013JD020175,
2014. a
Lauvaux, T., Schuh, A. E., Uliasz, M., Richardson, S., Miles, N., Andrews, A. E., Sweeney, C., Diaz, L. I., Martins, D., Shepson, P. B., and Davis, K. J.: Constraining the CO2 budget of the corn belt: exploring uncertainties from the assumptions in a mesoscale inverse system, Atmos. Chem. Phys., 12, 337–354, https://doi.org/10.5194/acp-12-337-2012, 2012. a, b, c
Loh, Z. M., Law, R. M., Ziehn, T., van der Schoot M. V., Krummel, P. B.,
Steele, L. P., Etheridge, D. M., Spencer, D. A., Gregory, R. L., Langenfelds,
R. L., Stavert, A. R., and Thornton, D. P.: The Australian Greenhouse Gas
Observation Network: Current status and vision for the future. 10th
International Carbon Dioxide Conference (ICDC10), 21–25 August
2017, Interlaken, Switzerland,
available at: http://www.icdc10.unibe.ch/unibe/portal/fak_naturwis/micro_icdc10/content/e342182/e604227/e604229/files623284/Loh_Zoe.pdf (last access: 25 May 2021),
2017. a
Lopez, M., Schmidt, M., Ramonet, M., Bonne, J.-L., Colomb, A., Kazan, V., Laj, P., and Pichon, J.-M.: Three years of semicontinuous greenhouse gas measurements at the Puy de Dôme station (central France), Atmos. Meas. Tech., 8, 3941–3958, https://doi.org/10.5194/amt-8-3941-2015, 2015. a
Louis, J. F.: A parametric model of vertical eddy flux in the atmosphere,
Bound.-Lay. Meteorol., 17, 187–202, 1979. a
Masarie, K. A., Peters, W., Jacobson, A. R., and Tans, P. P.: ObsPack: a framework for the preparation, delivery, and attribution of atmospheric greenhouse gas measurements, Earth Syst. Sci. Data, 6, 375–384, https://doi.org/10.5194/essd-6-375-2014, 2014. a
Morgui, J. A., Agueda, A., Batet, O., Curcoll, R., Ealo, M., G. C.,
Occhipinti, P., Sanchez-Garcia, L., Arias, R., and Rodo, X.: ClimaDat: A
long-term network to study at different scales climatic processes and
interactions between climatic compartments, Geophys. Res. Abstr.,
EGU13-10265, EGU General Assembly 2013, Vienna, Austria, 2013. a, b, c, d
Morino, I., Matsuzaki, T., and Horikawa, M.: TCCON data from Tsukuba (JP),
125HR, Release GGG2014.R1, https://doi.org/10.14291/TCCON.GGG2014.TSUKUBA02.R1/ 1241486,
2016a. a
Morino, I., Yokozeki, N., Matsuzaki, T., and Horikawa, M.: TCCON data from
Rikubetsu (JP), Release GGG2014.R1,
https://doi.org/10.14291/TCCON.GGG2014.TSUKUBA02.R1/ 1241486, 2016b. a
Necki, J., Schmidt, M., Rozanski, K., Zimnoch, M., Korus, A., Lasa, J., Graul,
R., and Levin, I.: Six-year record of atmospheric carbon dioxide and methane
at a high-altitude mountain site in Poland, Tellus B, 55, 94–104,
https://doi.org/10.1034/j.1600-0889.2003.01446.x, 2003. a
Noh, Y., Cheon, W., Hong, S., and Raasch, S.: Improvement of the K-profile
model for the planetary boundary layer based on large eddy simulation data,
Bound.-Lay. Meteorol., 107, 401–427, https://doi.org/10.1023/A:1022146015946,
2003. a
Notholt, J., Petri, C., Warneke, T., Deutscher, N. M., Palm, M., Buschmann, M.,
Weinzierl, C., Macatangay, R. C., and Grupe, P.: TCCON data from Bremen (DE),
Release GGG2014.R0, https://doi.org/10.14291/TCCON.GGG2014.BREMEN01.R0/ 1149275, 2014. a
O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C., Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J., Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T., Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval algorithm – Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5, 99–121, https://doi.org/10.5194/amt-5-99-2012, 2012. a
Pal, S.: ACT-America: Profile-based Planetary Boundary Layer Heights, Eastern
USA, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1706, 2019. a
Pal, S. and Davis, K.: ACT-America Field Campaign Catalogue, ORNL DAAC, Oak
Ridge, Tennessee, USA,
available at: https://actamerica.ornl.gov/campaigns.html (last access: 25 May 2021), 2020. a
Pal, S., Davis, K. J., Lauvaux, T., Browell, E. V., Gaudet, B. J., and Stauffer, D.:
Observations of Greenhouse Gas Changes Across Summer Frontal Boundaries in
the Eastern United States, J. Geophys. Res.-Atmos., 125, e2019JD030526,
https://doi.org/10.1029/2019JD030526, 2020. a, b, c, d, e, f, g, h, i, j, k, l
Patra, P. K., Law, R. M., Peters, W., Roedenbeck, C., Takigawa, M., Aulagnier,
C., Baker, I., Bergmann, D. J., Bousquet, P., Brandt, J., Bruhwiler, L.,
Cameron-Smith, P. J., Christensen, J. H., Delage, F., Denning, A. S., Fan,
S., Geels, C., Houweling, S., Imasu, R., Karstens, U., Kawa, S. R., Kleist,
J., Krol, M. C., Lin, S. J., Lokupitiya, R., Maki, T., Maksyutov, S., Niwa,
Y., Onishi, R., Parazoo, N., Pieterse, G., Rivier, L., Satoh, M., Serrar, S.,
Taguchi, S., Vautard, R., Vermeulen, A. T., and Zhu, Z.: TransCom model
simulations of hourly atmospheric CO2: Analysis of synoptic-scale variations
for the period 2002–2003, Global Biogeochem. Cycles, 22, gB4013,
https://doi.org/10.1029/2007GB003081, 2008. a, b
Peterson, J., Komhyr, W., Waterman, L., Gammon, R., Thoning, K., and Conway,
T.: Atmospheric CO2 variations at Barrow, Alaska,1973-1982, J.
Atmos. Chem., 4, 491–510, https://doi.org/10.1007/BF00053848, 1986. a
Pillai, D., Gerbig, C., Kretschmer, R., Beck, V., Karstens, U., Neininger, B., and Heimann, M.: Comparing Lagrangian and Eulerian models for CO2 transport – a step towards Bayesian inverse modeling using WRF/STILT-VPRM, Atmos. Chem. Phys., 12, 8979–8991, https://doi.org/10.5194/acp-12-8979-2012, 2012. a
Polavarapu, S. M., Neish, M., Tanguay, M., Girard, C., de Grandpré, J., Semeniuk, K., Gravel, S., Ren, S., Roche, S., Chan, D., and Strong, K.: Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO2, Atmos. Chem. Phys., 16, 12005–12038, https://doi.org/10.5194/acp-16-12005-2016, 2016. a, b, c, d, e
Putman, W. M. and Lin, S.-H.: Finite-volume transport on various cubed-sphere
grids, J. Comput. Phys., 227, 55–78,
https://doi.org/10.1016/j.jcp.2007.07.022, 2007. a
Ramonet, M., Ciais, P., Aalto, T., Aulagnier, C., Chevallier, F., Cipriano, D.,
Conway, T. J., Haszpra, L., Kazan, V., Meinhardt, F., Paris, J.-D., Schmidt,
M., Simmonds, P., Xueref-Remy, I., and Necki, J. N.: A recent build-up of
atmospheric CO2 over Europe. Part 1: observed signals and possible
explanations, Tellus B, 62,
1–13, https://doi.org/10.1111/j.1600-0889.2009.00442.x, 2010. a
Rayner, P. J., Michalak, A. M., and Chevallier, F.: Fundamentals of data assimilation applied to biogeochemistry, Atmos. Chem. Phys., 19, 13911–13932, https://doi.org/10.5194/acp-19-13911-2019, 2019. a
Ringler, T., Ju, L., and Gunzburger, M.: A multiresolution method for climate
system modeling: application of spherical centroidal Voronoi tessellations,
Ocean Dynam., 58, 475–498, https://doi.org/10.1007/s10236-008-0157-2, 2008. a
Ringler, T. D., Thuburn, J., Klemp, J. B., and Skamarock, W. C.: A unified
approach to energy conservation and potential vorticity dynamics for
arbitrarily-structured C-grids, J. Comput. Phys., 229,
3065–3090, https://doi.org/10.1016/j.jcp.2009.12.007, 2010. a
Sarrat, C., Noilhan, J., Lacarrere, P., Donier, S., Lac, C., Calvet, J. C.,
Dolman, A. J., Gerbig, C., Neininger, B., Ciais, P., Paris, J. D., Boumard,
F., Ramonet, M., and Butet, A.: Atmospheric CO2 modeling at the regional
scale: Application to the CarboEurope Regional Experiment, J.
Geophys. Res.-Atmos., 112, D12105, https://doi.org/10.1029/2006JD008107, 2007. a
Schibig, M. F., Steinbacher, M., Buchmann, B., van der Laan-Luijkx, I. T., van der Laan, S., Ranjan, S., and Leuenberger, M. C.: Comparison of continuous in situ CO2 observations at Jungfraujoch using two different measurement techniques, Atmos. Meas. Tech., 8, 57–68, https://doi.org/10.5194/amt-8-57-2015, 2015. a
Schmidt, M., Graul, R., Sartorius, H., and Levin, I.: The Schauinsland CO2
record: 30 years of continental observations and their implications for the
variability of the European CO2 budget, J. Geophys.
Res.-Atmos., 108, 4619, https://doi.org/10.1029/2002JD003085, 2003. a
Schuh, A. E., Lauvaux, T., West, T. O., Denning, A. S., Davis, K. J., Miles,
N., Richardson, S., Uliasz, M., Lokupitiya, E., Cooley, D., Andrews, A., and
Ogle, S.: Evaluating atmospheric CO2 inversions at multiple scales over a
highly inventoried agricultural landscape, Global Change Biol., 19,
1424–1439, https://doi.org/10.1111/gcb.12141, 2013. a
Schuh, A. E., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K.,
Chevallier, F., Crowell, S., Davis, K. J., Deng, F., Denning, S., Feng, L.,
Jones, D., Liu, J., and Palmer, I, P.: Quantifying the Impact of Atmospheric
Transport Uncertainty on CO2 Surface Flux Estimates, Global Biogeochem.
Cycles, 33, 484–500, https://doi.org/10.1029/2018GB006086, 2019. a
Sherlock, V., Connor, B., Robinson, J., Shiona, H., Smale, D., and Pollard,
D. F.: TCCON data from Lauder (NZ), 120HR, Release GGG2014.R0,
https://doi.org/10.14291/TCCON.GGG2014.LAUDER01.R0/ 1149293, 2014. a
Skamarock, W. C. and Gassmann, A.: Conservative Transport Schemes for
Spherical Geodesic Grids: High-Order Flux Operators for ODE-Based Time
Integration, Mon. Weather Rev., 139, 2962–2975,
https://doi.org/10.1175/MWR-D-10-05056.1, 2011. a, b, c, d
Stephens, B. B., Miles, N. L., Richardson, S. J., Watt, A. S., and Davis, K. J.: Atmospheric CO2 monitoring with single-cell NDIR-based analyzers, Atmos. Meas. Tech., 4, 2737–2748, https://doi.org/10.5194/amt-4-2737-2011, 2011. a, b, c
Sussmann, R. and Rettinger, M.: TCCON data from Garmisch (DE), Release
GGG2014.R0, https://doi.org/10.14291/TCCON.GGG2014.GARMISCH01.R0/ 1149299, 2015. a
Thoning, K., Tans, P., and Komhyr, W.: Atmospheric carbon dioxide at Mauna Loa
Observatory, 2. Analysis of the NOAA/GMCC data, 1974–1985, J.
Geophys. Res.-Atmos., 94, 8549–8565,
https://doi.org/10.1029/JD094iD06p08549, 1989. a
Thuburn, J.: Rossby wave dispersion on the C-grid, Atmos. Sci.
Lett., 8, 37–42, https://doi.org/10.1002/asl.148, 2007. a
Tsutsumi, Y., Matsueda, H., and Nishioka, S.: Consistency of the CO2 primary
standards in JMA, 12th WMO/IAEA meeting of experts on carbon dioxide
concentration and related tracers measurement techniques, Toronto, Canada,
15–18 September 2003, Global Atmosphere Watch Report No. 161,
WMO/TD-No.1275, 2005. a, b, c
Vermeulen, A. T., Hensen, A., Popa, M. E., van den Bulk, W. C. M., and Jongejan, P. A. C.: Greenhouse gas observations from Cabauw Tall Tower (1992–2010), Atmos. Meas. Tech., 4, 617–644, https://doi.org/10.5194/amt-4-617-2011, 2011. a
Walko, R. L. and Avissar, R.: The Ocean-Land-Atmosphere Model (OLAM). Part I:
Shallow-Water Tests, Mon. Weather Rev., 136, 4033–4044,
https://doi.org/10.1175/2008MWR2522.1, 2008a. a
Walko, R. L. and Avissar, R.: The Ocean-Land-Atmosphere Model (OLAM). Part II:
Formulation and Tests of the Nonhydrostatic Dynamic Core, Mon. Weather
Rev., 136, 4045–4062, https://doi.org/10.1175/2008MWR2523.1,
2008b. a
Warneke, T., Messerschmidt, J., Notholt, J., Weinzierl, C., Deutscher, N. M.,
Petri, C., and Grupe, P.: TCCON data from Orléans (FR), Release GGG2014.R0,
https://doi.org/10.14291/TCCON.GGG2014.ORLEANS01.R0/ 1149276, 2014. a
Wennberg, P. O., Roehl, C. M., Wunch, D., Toon, G. C., Blavier, J.-F.,
Washenfelder, R., Keppel-Aleks, G., Allen, N. T., and Ayers, J.: TCCON data
from Park Falls (US), Release GGG2014.R0,
https://doi.org/10.14291/TCCON.GGG2014.PARKFALLS01.R0/ 1149161, 2014a. a
Wennberg, P. O., Wunch, D., Roehl, C. M., Blavier, J.-F., Toon, G. C., Allen,
N. T., Dowell, P., Teske, K., Martin, C., and Martin, J.: TCCON data from
Lamont (US), Release GGG2014.R0,
https://doi.org/10.14291/TCCON.GGG2014.LAMONT01.R0/ 1149159, 2014b. a
Williamson, D.: Semi-Lagrangian moisture transport in the NMC spectral model,
Tells A, 42, 413–428,
https://doi.org/10.3402/tellusa.v42i4.11887, 1990. a
Wilson, P.: Insight into the Carbon Cycle from Continuous Measurements of
Oxygen and Carbon Dioxide at Weybourne Atmospheric Observatory, UK,, PhD
thesis, University of East Anglia, Norwich, UK, 2013. a
Wunch, D., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Stephens, B. B., Fischer, M. L., Uchino, O., Abshire, J. B., Bernath, P., Biraud, S. C., Blavier, J.-F. L., Boone, C., Bowman, K. P., Browell, E. V., Campos, T., Connor, B. J., Daube, B. C., Deutscher, N. M., Diao, M., Elkins, J. W., Gerbig, C., Gottlieb, E., Griffith, D. W. T., Hurst, D. F., Jiménez, R., Keppel-Aleks, G., Kort, E. A., Macatangay, R., Machida, T., Matsueda, H., Moore, F., Morino, I., Park, S., Robinson, J., Roehl, C. M., Sawa, Y., Sherlock, V., Sweeney, C., Tanaka, T., and Zondlo, M. A.: Calibration of the Total Carbon Column Observing Network using aircraft profile data, Atmos. Meas. Tech., 3, 1351–1362, https://doi.org/10.5194/amt-3-1351-2010, 2010.
a
Zheng, T., Nassar, R., and Baxter, M.: Estimating power plant CO2 emission
using OCO-2 XCO2 and high resolution WRF-Chem simulations, Environ.
Res. Lett., 14, 085001, https://doi.org/10.1088/1748-9326/ab25ae, 2019. a, b
Zheng, T., French, N. H. F., and Baxter, M.: Development of the WRF-CO2 4D-Var assimilation system v1.0, Geosci. Model Dev., 11, 1725–1752, https://doi.org/10.5194/gmd-11-1725-2018, 2018. a, b
Short summary
Carbon dioxide is the most important greenhouse gas. We develop the numerical model that represents carbon dioxide transport in the atmosphere. This model development is based on the MPAS model, which has a variable-resolution capability. The purpose of developing carbon dioxide transport in MPAS is to allow for high-resolution transport model simulation that is not limited by the lateral boundaries. It will also form the base for a future development of MPAS-based carbon inversion system.
Carbon dioxide is the most important greenhouse gas. We develop the numerical model that...
