Articles | Volume 11, issue 5
https://doi.org/10.5194/gmd-11-1725-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-11-1725-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
04 May 2018
Development and technical paper |
| 04 May 2018
Development of the WRF-CO2 4D-Var assimilation system v1.0
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
Nancy H. F. French
Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI, USA
Martin Baxter
Department of Earth and Atmospheric Sciences, Central Michigan University, Mount Pleasant, MI, USA
Related authors
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
Short summary
Short summary
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.
Tao Zheng, Sha Feng, Kenneth J. Davis, Sandip Pal, and Josep-Anton Morguí
Geosci. Model Dev., 14, 3037–3066, https://doi.org/10.5194/gmd-14-3037-2021, https://doi.org/10.5194/gmd-14-3037-2021, 2021
Short summary
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.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
Short summary
Short summary
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.
Charles Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-172, https://doi.org/10.5194/essd-2021-172, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 4 million km2 in Alaska and northwestern Canada during 2017, 2018, and 2019. This paper summarizes those results and gives details on ~80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan Berner, Amy Breen, Abhishek Chatterjee, Scott Davidson, Gerald Frost, Teresa Hollingsworth, Go Iwahana, Randi Jandt, Anja Kade, Tatiana Loboda, Matt Macander, Michelle Mack, Charles Miller, Eric Miller, Susan Natali, Martha Raynolds, Adrian Rocha, Shiro Tsuyuzaki, Craig Tweedie, Donald Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-222, https://doi.org/10.5194/essd-2023-222, 2023
Preprint under review for ESSD
Short summary
Short summary
The Arctic tundra is experiencing widespread physical and biological changes largely in response to warming. Yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and study compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which pulls together field datasets and includes vegetation, active layer, and fire-related properties.
Stefano Potter, Sol Cooperdock, Sander Veraverbeke, Xanthe Walker, Michelle C. Mack, Scott J. Goetz, Jennifer Baltzer, Laura Bourgeau-Chavez, Arden Burrell, Catherine Dieleman, Nancy French, Stijn Hantson, Elizabeth E. Hoy, Liza Jenkins, Jill F. Johnstone, Evan S. Kane, Susan M. Natali, James T. Randerson, Merritt R. Turetsky, Ellen Whitman, Elizabeth Wiggins, and Brendan M. Rogers
Biogeosciences, 20, 2785–2804, https://doi.org/10.5194/bg-20-2785-2023, https://doi.org/10.5194/bg-20-2785-2023, 2023
Short summary
Short summary
Here we developed a new burned-area detection algorithm between 2001–2019 across Alaska and Canada at 500 m resolution. We estimate 2.37 Mha burned annually between 2001–2019 over the domain, emitting 79.3 Tg C per year, with a mean combustion rate of 3.13 kg C m−2. We found larger-fire years were generally associated with greater mean combustion. The burned-area and combustion datasets described here can be used for local- to continental-scale applications of boreal fire science.
Tao Zheng, Sha Feng, Kenneth J. Davis, Sandip Pal, and Josep-Anton Morguí
Geosci. Model Dev., 14, 3037–3066, https://doi.org/10.5194/gmd-14-3037-2021, https://doi.org/10.5194/gmd-14-3037-2021, 2021
Short summary
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.
T. T. van Leeuwen, G. R. van der Werf, A. A. Hoffmann, R. G. Detmers, G. Rücker, N. H. F. French, S. Archibald, J. A. Carvalho Jr., G. D. Cook, W. J. de Groot, C. Hély, E. S. Kasischke, S. Kloster, J. L. McCarty, M. L. Pettinari, P. Savadogo, E. C. Alvarado, L. Boschetti, S. Manuri, C. P. Meyer, F. Siegert, L. A. Trollope, and W. S. W. Trollope
Biogeosciences, 11, 7305–7329, https://doi.org/10.5194/bg-11-7305-2014, https://doi.org/10.5194/bg-11-7305-2014, 2014
Y. Huang, S. Wu, M. K. Dubey, and N. H. F. French
Atmos. Chem. Phys., 13, 6329–6343, https://doi.org/10.5194/acp-13-6329-2013, https://doi.org/10.5194/acp-13-6329-2013, 2013
Related subject area
Atmospheric sciences
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Effects of vertical grid spacing on the climate simulated in the ICON-Sapphire global storm-resolving model
Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO2 v7.3
Impacts of updated reaction kinetics on the global GEOS-Chem simulation of atmospheric chemistry
Spatial spin-up of precipitation in limited-area convection-permitting simulations over North America using the CRCM6/GEM5.0 model
Sensitivity of atmospheric rivers to aerosol treatment in regional climate simulations: insights from the AIRA identification algorithm
The implementation of dust mineralogy in COSMO5.05-MUSCAT
Implementation of the ISORROPIA-lite aerosol thermodynamics model into the EMAC chemistry climate model (based on MESSy v2.55): implications for aerosol composition and acidity
Evaluation of surface shortwave downward radiation forecasts by the numerical weather prediction model AROME
GEO4PALM v1.1: an open-source geospatial data processing toolkit for the PALM model system
Modeling collision–coalescence in particle microphysics: numerical convergence of mean and variance of precipitation in cloud simulations using the University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1
Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1
Impacts of a double-moment bulk cloud microphysics scheme (NDW6-G23) on aerosol fields in NICAM.19 with a global 14 km grid resolution
Sensitivity of air quality model responses to emission changes: comparison of results based on four EU inventories through FAIRMODE benchmarking methodology
A simple and realistic aerosol emission approach for use in the Thompson–Eidhammer microphysics scheme in the NOAA UFS Weather Model (version GSL global-24Feb2022)
On the formation of biogenic secondary organic aerosol in chemical transport models: an evaluation of the WRF-CHIMERE (v2020r2) model with a focus over the Finnish boreal forest
The first application of a numerically exact, higher-order sensitivity analysis approach for atmospheric modelling: implementation of the hyperdual-step method in the Community Multiscale Air Quality Model (CMAQ) version 5.3.2
GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement
Analysis of the GEFS-Aerosols annual budget to better understand aerosol predictions simulated in the model
A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3
BoundaryLayerDynamics.jl v1.0: a modern codebase for atmospheric boundary-layer simulations
Investigating Ground-Level Ozone Pollution in Semi-Arid and Arid Regions of Arizona Using WRF-Chem v4.4 Modeling
The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1
FUME 2.0 – Flexible Universal processor for Modeling Emissions
Assessment of tropospheric ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Application of regional meteorology and air quality models based on MIPS CPU Platform
Spherical air mass factors in one and two dimensions with SASKTRAN 1.6.0
An improved version of the piecewise parabolic method advection scheme: description and performance assessment in a bidimensional test case with stiff chemistry in toyCTM v1.0.1
INCHEM-Py v1.2: a community box model for indoor air chemistry
Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemistry-transport model using Cloud-J v7.3e
Representation of atmosphere-induced heterogeneity in land–atmosphere interactions in E3SM–MMFv2
How the meteorological spectral nudging impacts on aerosol radiation clouds interactions?
Assimilation of GNSS Tropospheric Gradients into the Weather Research and Forecasting Model Version 4.4.1
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta): ensemble of 3D ensemble-variational (En-3DEnVar) assimilations
Simulations of 7Be and 10Be with the GEOS-Chem global model v14.0.2 using state-of-the-art production rates
Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 2: Influence of uncertainty factors
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Short summary
In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
Short summary
Short summary
A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
Short summary
Short summary
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Short summary
The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
Short summary
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
Short summary
This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Short summary
PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary
Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
Short summary
Short summary
Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
Short summary
Short summary
We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
Short summary
Short summary
The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen
Geosci. Model Dev., 17, 1667–1688, https://doi.org/10.5194/gmd-17-1667-2024, https://doi.org/10.5194/gmd-17-1667-2024, 2024
Short summary
Short summary
We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
Hauke Schmidt, Sebastian Rast, Jiawei Bao, Amrit Cassim, Shih-Wei Fang, Diego Jimenez-de la Cuesta, Paul Keil, Lukas Kluft, Clarissa Kroll, Theresa Lang, Ulrike Niemeier, Andrea Schneidereit, Andrew I. L. Williams, and Bjorn Stevens
Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, https://doi.org/10.5194/gmd-17-1563-2024, 2024
Short summary
Short summary
A recent development in numerical simulations of the global atmosphere is the increase in horizontal resolution to grid spacings of a few kilometers. However, the vertical grid spacing of these models has not been reduced at the same rate as the horizontal grid spacing. Here, we assess the effects of much finer vertical grid spacings, in particular the impacts on cloud quantities and the atmospheric energy balance.
Tao Zheng, Sha Feng, Jeffrey Steward, Xiaoxu Tian, David Baker, and Martin Baxter
Geosci. Model Dev., 17, 1543–1562, https://doi.org/10.5194/gmd-17-1543-2024, https://doi.org/10.5194/gmd-17-1543-2024, 2024
Short summary
Short summary
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.
Kelvin H. Bates, Mathew J. Evans, Barron H. Henderson, and Daniel J. Jacob
Geosci. Model Dev., 17, 1511–1524, https://doi.org/10.5194/gmd-17-1511-2024, https://doi.org/10.5194/gmd-17-1511-2024, 2024
Short summary
Short summary
Accurate representation of rates and products of chemical reactions in atmospheric models is crucial for simulating concentrations of pollutants and climate forcers. We update the widely used GEOS-Chem atmospheric chemistry model with reaction parameters from recent compilations of experimental data and demonstrate the implications for key atmospheric chemical species. The updates decrease tropospheric CO mixing ratios and increase stratospheric nitrogen oxide mixing ratios, among other changes.
François Roberge, Alejandro Di Luca, René Laprise, Philippe Lucas-Picher, and Julie Thériault
Geosci. Model Dev., 17, 1497–1510, https://doi.org/10.5194/gmd-17-1497-2024, https://doi.org/10.5194/gmd-17-1497-2024, 2024
Short summary
Short summary
Our study addresses a challenge in dynamical downscaling using regional climate models, focusing on the lack of small-scale features near the boundaries. We introduce a method to identify this “spatial spin-up” in precipitation simulations. Results show spin-up distances up to 300 km, varying by season and driving variable. Double nesting with comprehensive variables (e.g. microphysical variables) offers advantages. Findings will help optimize simulations for better climate projections.
Eloisa Raluy-López, Juan Pedro Montávez, and Pedro Jiménez-Guerrero
Geosci. Model Dev., 17, 1469–1495, https://doi.org/10.5194/gmd-17-1469-2024, https://doi.org/10.5194/gmd-17-1469-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) represent a significant source of water but are also related to extreme precipitation events. Here, we present a new regional-scale AR identification algorithm and apply it to three simulations that include aerosol interactions at different levels. The results show that aerosols modify the intensity and trajectory of ARs and redistribute the AR-related precipitation. Thus, the correct inclusion of aerosol effects is important in the simulation of AR behavior.
Sofía Gómez Maqueo Anaya, Dietrich Althausen, Matthias Faust, Holger Baars, Bernd Heinold, Julian Hofer, Ina Tegen, Albert Ansmann, Ronny Engelmann, Annett Skupin, Birgit Heese, and Kerstin Schepanski
Geosci. Model Dev., 17, 1271–1295, https://doi.org/10.5194/gmd-17-1271-2024, https://doi.org/10.5194/gmd-17-1271-2024, 2024
Short summary
Short summary
Mineral dust aerosol particles vary greatly in their composition depending on source region, which leads to different physicochemical properties. Most atmosphere–aerosol models consider mineral dust aerosols to be compositionally homogeneous, which ultimately increases model uncertainty. Here, we present an approach to explicitly consider the heterogeneity of the mineralogical composition for simulations of the Saharan atmospheric dust cycle with regard to dust transport towards the Atlantic.
Alexandros Milousis, Alexandra P. Tsimpidi, Holger Tost, Spyros N. Pandis, Athanasios Nenes, Astrid Kiendler-Scharr, and Vlassis A. Karydis
Geosci. Model Dev., 17, 1111–1131, https://doi.org/10.5194/gmd-17-1111-2024, https://doi.org/10.5194/gmd-17-1111-2024, 2024
Short summary
Short summary
This study aims to evaluate the newly developed ISORROPIA-lite aerosol thermodynamic module within the EMAC model and explore discrepancies in global atmospheric simulations of aerosol composition and acidity by utilizing different aerosol phase states. Even though local differences were found in regions where the RH ranged from 20 % to 60 %, on a global scale the results are similar. Therefore, ISORROPIA-lite can be a reliable and computationally effective alternative to ISORROPIA II in EMAC.
Marie-Adèle Magnaldo, Quentin Libois, Sébastien Riette, and Christine Lac
Geosci. Model Dev., 17, 1091–1109, https://doi.org/10.5194/gmd-17-1091-2024, https://doi.org/10.5194/gmd-17-1091-2024, 2024
Short summary
Short summary
With the worldwide development of the solar energy sector, the need for reliable solar radiation forecasts has significantly increased. However, meteorological models that predict, among others things, solar radiation have errors. Therefore, we wanted to know in which situtaions these errors are most significant. We found that errors mostly occur in cloudy situations, and different errors were highlighted depending on the cloud altitude. Several potential sources of errors were identified.
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024, https://doi.org/10.5194/gmd-17-815-2024, 2024
Short summary
Short summary
GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
Piotr Zmijewski, Piotr Dziekan, and Hanna Pawlowska
Geosci. Model Dev., 17, 759–780, https://doi.org/10.5194/gmd-17-759-2024, https://doi.org/10.5194/gmd-17-759-2024, 2024
Short summary
Short summary
In computer simulations of clouds it is necessary to model the myriad of droplets that constitute a cloud. A popular method for this is to use so-called super-droplets (SDs), each representing many real droplets. It has remained a challenge to model collisions of SDs. We study how precipitation in a cumulus cloud depends on the number of SDs. Surprisingly, we do not find convergence in mean precipitation even for numbers of SDs much larger than typically used in simulations.
Roya Ghahreman, Wanmin Gong, Paul A. Makar, Alexandru Lupu, Amanda Cole, Kulbir Banwait, Colin Lee, and Ayodeji Akingunola
Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, https://doi.org/10.5194/gmd-17-685-2024, 2024
Short summary
Short summary
The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Daisuke Goto, Tatsuya Seiki, Kentaroh Suzuki, Hisashi Yashiro, and Toshihiko Takemura
Geosci. Model Dev., 17, 651–684, https://doi.org/10.5194/gmd-17-651-2024, https://doi.org/10.5194/gmd-17-651-2024, 2024
Short summary
Short summary
Global climate models with coarse grid sizes include uncertainties about the processes in aerosol–cloud–precipitation interactions. To reduce these uncertainties, here we performed numerical simulations using a new version of our global aerosol transport model with a finer grid size over a longer period than in our previous study. As a result, we found that the cloud microphysics module influences the aerosol distributions through both aerosol wet deposition and aerosol–cloud interactions.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, Enrico Pisoni, and Bertrand Bessagnet
Geosci. Model Dev., 17, 587–606, https://doi.org/10.5194/gmd-17-587-2024, https://doi.org/10.5194/gmd-17-587-2024, 2024
Short summary
Short summary
In our study the robustness of the model responses to emission reductions in the EU is assessed when the emission data are changed. Our findings are particularly important to better understand the uncertainties associated to the emission inventories and how these uncertainties impact the level of accuracy of the resulting air quality modelling, which is a key for designing air quality plans. Also crucial is the choice of indicator to avoid misleading interpretations of the results.
Haiqin Li, Georg A. Grell, Ravan Ahmadov, Li Zhang, Shan Sun, Jordan Schnell, and Ning Wang
Geosci. Model Dev., 17, 607–619, https://doi.org/10.5194/gmd-17-607-2024, https://doi.org/10.5194/gmd-17-607-2024, 2024
Short summary
Short summary
We developed a simple and realistic method to provide aerosol emissions for aerosol-aware microphysics in a numerical weather forecast model. The cloud-radiation differences between the experimental (EXP) and control (CTL) experiments responded to the aerosol differences. The strong positive precipitation biases over North America and Europe from the CTL run were significantly reduced in the EXP run. This study shows that a realistic representation of aerosol emissions should be considered.
Giancarlo Ciarelli, Sara Tahvonen, Arineh Cholakian, Manuel Bettineschi, Bruno Vitali, Tuukka Petäjä, and Federico Bianchi
Geosci. Model Dev., 17, 545–565, https://doi.org/10.5194/gmd-17-545-2024, https://doi.org/10.5194/gmd-17-545-2024, 2024
Short summary
Short summary
The terrestrial ecosystem releases large quantities of biogenic gases in the Earth's Atmosphere. These gases can effectively be converted into so-called biogenic aerosol particles and, eventually, affect the Earth's climate. Climate prediction varies greatly depending on how these processes are represented in model simulations. In this study, we present a detailed model evaluation analysis aimed at understanding the main source of uncertainty in predicting the formation of biogenic aerosols.
Jiachen Liu, Eric Chen, and Shannon L. Capps
Geosci. Model Dev., 17, 567–585, https://doi.org/10.5194/gmd-17-567-2024, https://doi.org/10.5194/gmd-17-567-2024, 2024
Short summary
Short summary
Air pollution harms human life and ecosystems, but its sources are complex. Scientists and policy makers use air pollution models to advance knowledge and inform control strategies. We implemented a recently developed numeral system to relate any set of model inputs, like pollutant emissions from a given activity, to all model outputs, like concentrations of pollutants harming human health. This approach will be straightforward to update when scientists discover new processes in the atmosphere.
Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng
Geosci. Model Dev., 17, 399–413, https://doi.org/10.5194/gmd-17-399-2024, https://doi.org/10.5194/gmd-17-399-2024, 2024
Short summary
Short summary
Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.
Li Pan, Partha S. Bhattacharjee, Li Zhang, Raffaele Montuoro, Barry Baker, Jeff McQueen, Georg A. Grell, Stuart A. McKeen, Shobha Kondragunta, Xiaoyang Zhang, Gregory J. Frost, Fanglin Yang, and Ivanka Stajner
Geosci. Model Dev., 17, 431–447, https://doi.org/10.5194/gmd-17-431-2024, https://doi.org/10.5194/gmd-17-431-2024, 2024
Short summary
Short summary
A GEFS-Aerosols simulation was conducted from 1 September 2019 to 30 September 2020 to evaluate the model performance of GEFS-Aerosols. The purpose of this study was to understand how aerosol chemical and physical processes affect ambient aerosol concentrations by placing aerosol wet deposition, dry deposition, reactions, gravitational deposition, and emissions into the aerosol mass balance equation.
Sean Raffuse, Susan O'Neill, and Rebecca Schmidt
Geosci. Model Dev., 17, 381–397, https://doi.org/10.5194/gmd-17-381-2024, https://doi.org/10.5194/gmd-17-381-2024, 2024
Short summary
Short summary
Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Manuel F. Schmid, Marco G. Giometto, Gregory A. Lawrence, and Marc B. Parlange
Geosci. Model Dev., 17, 321–333, https://doi.org/10.5194/gmd-17-321-2024, https://doi.org/10.5194/gmd-17-321-2024, 2024
Short summary
Short summary
Turbulence-resolving flow models have strict performance requirements, as simulations often run for weeks using hundreds of processes. Many flow scenarios also require the flexibility to modify physical and numerical models for problem-specific requirements. With a new code written in Julia we hope to make such adaptations easier without compromising on performance. In this paper we discuss the modeling approach and present validation and performance results.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-234, https://doi.org/10.5194/gmd-2023-234, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This research focuses on surface ozone (O3) pollution in Arizona, a historically air quality-challenged arid/semi-arid region in the US. The unique characteristics of semi-arid/arid regions, e.g., intense heat, minimal moisture, persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Marie-Noëlle Bouin, Cindy Lebeaupin Brossier, Sylvie Malardel, Aurore Voldoire, and César Sauvage
Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024, https://doi.org/10.5194/gmd-17-117-2024, 2024
Short summary
Short summary
In numerical models, the turbulent exchanges of heat and momentum at the air–sea interface are not represented explicitly but with parameterisations depending on the surface parameters. A new parameterisation of turbulent fluxes (WASP) has been implemented in the surface model SURFEX v8.1 and validated on four case studies. It combines a close fit to observations including cyclonic winds, a dependency on the wave growth rate, and the possibility of being used in atmosphere–wave coupled models.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
EGUsphere, https://doi.org/10.5194/egusphere-2023-2740, https://doi.org/10.5194/egusphere-2023-2740, 2024
Short summary
Short summary
For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure facilitating further processing to allow emission processing from continental to street scale.
Najmeh Kaffashzadeh and Abbas Ali Aliakbari Bidokhti
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-226, https://doi.org/10.5194/gmd-2023-226, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Reanalysis data have been widely used as an initial condition for the daily forecast of the atmosphere or boundary conditions in regional models, for the study of climate change, and as proxies to complement insufficient in situ measurements. This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2023-2962, https://doi.org/10.5194/egusphere-2023-2962, 2024
Short summary
Short summary
There are relatively limited researches on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPU, have distinct advantages in energy efficiency and scalability. In this study, the air quality modeling system can run stably on MIPS CPU platform, and the experiment results verify the stability of scientific computing on the platform. The work provides a technical foundation for the scientific application based on MIPS CPU platforms.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
Short summary
Short summary
This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Sylvain Mailler, Romain Pennel, Laurent Menut, and Arineh Cholakian
Geosci. Model Dev., 16, 7509–7526, https://doi.org/10.5194/gmd-16-7509-2023, https://doi.org/10.5194/gmd-16-7509-2023, 2023
Short summary
Short summary
We show that a new advection scheme named PPM + W (piecewise parabolic method + Walcek) offers geoscientific modellers an alternative, high-performance scheme designed for Cartesian-grid advection, with improved performance over the classical PPM scheme. The computational cost of PPM + W is not higher than that of PPM. With improved accuracy and controlled computational cost, this new scheme may find applications in chemistry-transport models, ocean models or atmospheric circulation models.
David R. Shaw, Toby J. Carter, Helen L. Davies, Ellen Harding-Smith, Elliott C. Crocker, Georgia Beel, Zixu Wang, and Nicola Carslaw
Geosci. Model Dev., 16, 7411–7431, https://doi.org/10.5194/gmd-16-7411-2023, https://doi.org/10.5194/gmd-16-7411-2023, 2023
Short summary
Short summary
Exposure to air pollution is one of the greatest risks to human health, and it is indoors, where we spend upwards of 90 % of our time, that our exposure is greatest. The INdoor CHEMical model in Python (INCHEM-Py) is a new, community-led box model that tracks the evolution and fate of atmospheric chemical pollutants indoors. We have shown the processes simulated by INCHEM-Py, its ability to model experimental data and how it may be used to develop further understanding of indoor air chemistry.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
Short summary
Short summary
Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Jungmin Lee, Walter M. Hannah, and David C. Bader
Geosci. Model Dev., 16, 7275–7287, https://doi.org/10.5194/gmd-16-7275-2023, https://doi.org/10.5194/gmd-16-7275-2023, 2023
Short summary
Short summary
Representing accurate land–atmosphere interaction processes is overlooked in weather and climate models. In this study, we propose three methods to represent land–atmosphere coupling in the Energy Exascale Earth System Model (E3SM) with the Multi-scale Modeling Framework (MMF) approach. In this study, we introduce spatially homogeneous and heterogeneous land–atmosphere interaction processes within the cloud-resolving model domain. Our 5-year simulations reveal only small differences.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-209, https://doi.org/10.5194/gmd-2023-209, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe and during the summer 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impact of two modelling processes able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Rohith Muraleedharan Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-202, https://doi.org/10.5194/gmd-2023-202, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Global Navigation Satellite Systems provide moisture observations through its densely distributed ground station network. In this research, we assimilated a new type of observation called tropospheric gradient observations, which was never incorporated into a weather model. Here, we have developed a forward operator for gradient observations and performed impact studies. Promising improvements were observed in the humidity fields of the model in the assimilation study.
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu
Geosci. Model Dev., 16, 7223–7235, https://doi.org/10.5194/gmd-16-7223-2023, https://doi.org/10.5194/gmd-16-7223-2023, 2023
Short summary
Short summary
The existing zenith tropospheric delay (ZTD) models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data point for modeling. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernández Baños, Yonggang G. Yu, Soyoung Ha, Yannick Trémolet, Thomas Auligné, Clementine Gas, Benjamin Ménétrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 16, 7123–7142, https://doi.org/10.5194/gmd-16-7123-2023, https://doi.org/10.5194/gmd-16-7123-2023, 2023
Short summary
Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Minjie Zheng, Hongyu Liu, Florian Adolphi, Raimund Muscheler, Zhengyao Lu, Mousong Wu, and Nønne L. Prisle
Geosci. Model Dev., 16, 7037–7057, https://doi.org/10.5194/gmd-16-7037-2023, https://doi.org/10.5194/gmd-16-7037-2023, 2023
Short summary
Short summary
The radionuclides 7Be and 10Be are useful tracers for atmospheric transport studies. Here we use the GEOS-Chem to simulate 7Be and 10Be with different production rates: the default production rate in GEOS-Chem and two from the state-of-the-art beryllium production model. We demonstrate that reduced uncertainties in the production rates can enhance the utility of 7Be and 10Be as tracers for evaluating transport and scavenging processes in global models.
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6833–6856, https://doi.org/10.5194/gmd-16-6833-2023, https://doi.org/10.5194/gmd-16-6833-2023, 2023
Short summary
Short summary
In addition to the dominant role of the PBL scheme on the results of the meteorological field, many factors in the model are influenced by large uncertainties. This study focuses on the uncertainties that influence numerical simulation results (including horizontal resolution, vertical resolution, near-surface scheme, initial and boundary conditions, underlying surface update, and update of model version), hoping to provide a reference for scholars conducting research on the model.
Cited articles
Alden, C. B., Miller, J. B., Gatti, L. V., Gloor, M. M., Guan, K., Michalak,
A. M., van der Laan-Luijkx, I. T., Touma, D., Andrews, A., Basso, L. S.,
Correia, C. S. C., Domingues, L. G., Joiner, J., Krol, M. C., Lyapustin,
A. I., Peters, W., Shiga, Y. P., Thoning, K., van der Velde, I. R., van
Leeuwen, T. T., Yadav, V., and Diffenbaugh, N. S.: Regional atmospheric CO2
inversion reveals seasonal and geographic differences in Amazon net biome
exchange, Global Change Biol., 22, 3427–3443, 2016. a, b
Appel, K. W., Napelenok, S. L., Foley, K. M., Pye, H. O. T., Hogrefe, C.,
Luecken, D. J., Bash, J. O., Roselle, S. J., Pleim, J. E., Foroutan, H.,
Hutzell, W. T., Pouliot, G. A., Sarwar, G., Fahey, K. M., Gantt, B., Gilliam,
R. C., Heath, N. K., Kang, D., Mathur, R., Schwede, D. B., Spero, T. L.,
Wong, D. C., and Young, J. O.: Description and evaluation of the Community
Multiscale Air Quality (CMAQ) modeling system version 5.1, Geosci. Model
Dev., 10, 1703–1732, https://doi.org/10.5194/gmd-10-1703-2017, 2017. a
Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data assimilation
for atmospheric CO2, Tellus B, 58, 359–365, 2006. a
Baker, D. F., Bösch, H., Doney, S. C., O'Brien, D., and Schimel, D. S.:
Carbon source/sink information provided by column CO2 measurements from
the Orbiting Carbon Observatory, Atmos. Chem. Phys., 10, 4145–4165,
https://doi.org/10.5194/acp-10-4145-2010, 2010. a, b
Barker, D., Huang, X.-Y., Liu, Z., Auligne, T., Zhang, X., Rugg, S., Ajjaji,
R., Bourgeois, A., Bray, J., Chen, Y., Demirtas, M., Guo, Y.-R., Henderson,
T., Huang, W., Lin, H.-C., Michalakes, J., Rizvi, S., and Zhang, X.: The
Weather Research and Forecasting Model's Community Variational/Ensemble Data
Assimilation System WRFDA, B. Am. Meteorol. Soc., 93, 831–843, 2012. a, b, c, d
Basu, S., Houweling, S., Peters, W., Sweeney, C., Machida, T., Maksyutov, S.,
Patra, P. K., Saito, R., Chevallier, F., Niwa, Y., Matsueda, H., and Sawa,
Y.: The seasonal cycle amplitude of total column CO2: Factors behind the
model-observation mismatch, J. Geophys. Res, 116, d23306, https://doi.org/10.1029/2011JD016124, 2011. a
Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I.,
Krummel, P., Steele, P., Langenfelds, R., Torn, M., Biraud, S., Stephens, B.,
Andrews, A., and Worthy, D.: Global CO2 fluxes estimated from GOSAT
retrievals of total column CO2, Atmos. Chem. Phys., 13, 8695–8717,
https://doi.org/10.5194/acp-13-8695-2013, 2013. a
Bocquet, M.: Toward Optimal Choices of Control Space Representation for
Geophysical Data Assimilation, Mon. Weather Rev., 137, 2331–2348, 2009. a
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G.
R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L.,
Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale,
C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry
models: current status and future prospects for coupled chemistry meteorology
models, Atmos. Chem. Phys., 15, 5325–5358,
https://doi.org/10.5194/acp-15-5325-2015, 2015. a
Bousquet, P., Ciais, P., Peylin, P., Ramonet, M., and Monfray, P.: Inverse
modeling of annual atmospheric CO2 sources and sinks 1. Method and control
inversion, J. Geophys. Res, 104, 26161–26178, 1999. a
Bruhwiler, L. M. P., Michalak, A. M., Peters, W., Baker, D. F., and Tans, P.:
An improved Kalman Smoother for atmospheric inversions, Atmos. Chem. Phys.,
5, 2691–2702, https://doi.org/10.5194/acp-5-2691-2005, 2005. a, b
Butler, M. P., Davis, K. J., Denning, A. S., and Kawa, S. R.: Using continental
observations in global atmospheric inversions of CO2: North American carbon
sources and sinks, Tellus B, 62, 550–572, 2010. a
Chan, E., Chan, D., Ishizawa, M., Vogel, F., Brioude, J., Delcloo, A., Wu,
Y., and Jin, B.: Description and evaluation of REFIST v1.0: a regional
greenhouse gas flux inversion system in Canada, Geosci. Model Dev. Discuss.,
https://doi.org/10.5194/gmd-2016-213, 2016. a
Chevallier, F.: Impact of correlated observation errors on inverted CO2
surface fluxes from OCO measurements, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL030463, 2007. a
Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Breon, F. M.,
Chedin, A., and Ciais, P.: Inferring CO2 sources and sinks from satellite
observations: Method and application to TOVS data, J. Geophys. Res., 110,
d24309, https://doi.org/10.1029/2005JD006390, 2005. a, b, c
Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E., Bousquet,
P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Froehlich, M., Gomez, A.,
Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R. L.,
Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morgui, J. A.,
Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y.,
Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and
Worthy, D.: CO2 surface fluxes at grid point scale estimated from a global 21
year reanalysis of atmospheric measurements, J. Geophys. Res., 115, d21307, https://doi.org/10.1029/2010JD013887, 2010. a
Chevallier, F., Viovy, N., Reichstein, M., and Ciais, P.: On the assignment of
prior errors in Bayesian inversions of CO2 surface fluxes, Geophys. Res.
Lett., 33, https://doi.org/10.1029/2006GL026496, 2006. a
Chou, M. D. and Suarez, M.: A solar radiation parameterization for atmospheric
studies, Tech. Rep. NASA/TM-1999-10460, Vol. 15, 38 pp., NASA, 1999. a
Ciais, P., Dolman, A. J., Bombelli, A., Duren, R., Peregon, A., Rayner, P.
J., Miller, C., Gobron, N., Kinderman, G., Marland, G., Gruber, N.,
Chevallier, F., Andres, R. J., Balsamo, G., Bopp, L., Bréon, F.-M., Broquet,
G., Dargaville, R., Battin, T. J., Borges, A., Bovensmann, H., Buchwitz, M.,
Butler, J., Canadell, J. G., Cook, R. B., DeFries, R., Engelen, R., Gurney,
K. R., Heinze, C., Heimann, M., Held, A., Henry, M., Law, B., Luyssaert, S.,
Miller, J., Moriyama, T., Moulin, C., Myneni, R. B., Nussli, C., Obersteiner,
M., Ojima, D., Pan, Y., Paris, J.-D., Piao, S. L., Poulter, B., Plummer, S.,
Quegan, S., Raymond, P., Reichstein, M., Rivier, L., Sabine, C., Schimel, D.,
Tarasova, O., Valentini, R., Wang, R., van der Werf, G., Wickland, D.,
Williams, M., and Zehner, C.: Current systematic carbon-cycle observations
and the need for implementing a policy-relevant carbon observing system,
Biogeosciences, 11, 3547–3602, https://doi.org/10.5194/bg-11-3547-2014, 2014. a
Deng, F., Jones, D. B. A., Henze, D. K., Bousserez, N., Bowman, K. W.,
Fisher, J. B., Nassar, R., O'Dell, C., Wunch, D., Wennberg, P. O., Kort, E.
A., Wofsy, S. C., Blumenstock, T., Deutscher, N. M., Griffith, D. W. T.,
Hase, F., Heikkinen, P., Sherlock, V., Strong, K., Sussmann, R., and Warneke,
T.: Inferring regional sources and sinks of atmospheric CO2 from GOSAT
XCO2 data, Atmos. Chem. Phys., 14, 3703–3727,
https://doi.org/10.5194/acp-14-3703-2014, 2014. a
Enting, I. G., Trudinger, C. M., and Francey, R. J.: A Synthesis Inversion of
the Concentration and Delta-C-13 of Atmospheric CO2, Tellus B, 47, 35–52,
1995. a
Freitas, S. R., Longo, K. M., Alonso, M. F., Pirre, M., Marecal, V., Grell,
G., Stockler, R., Mello, R. F., and Sánchez Gácita, M.: PREP-CHEM-SRC
– 1.0: a preprocessor of trace gas and aerosol emission fields for regional
and global atmospheric chemistry models, Geosci. Model Dev., 4, 419–433,
https://doi.org/10.5194/gmd-4-419-2011, 2011. a
French, N. H. F., de Groot, W. J., Jenkins, L. K., Rogers, B. M., Alvarado, E.,
Amiro, B., de Jong, B., Goetz, S., Hoy, E., Hyer, E., Keane, R., Law, B. E.,
McKenzie, D., McNulty, S. G., Ottmar, R., Perez-Salicrup, D. R., Randerson,
J., Robertson, K. M., and Turetsky, M.: Model comparisons for estimating
carbon emissions from North American wildland fire, J. Geophys. Res.,
116, https://doi.org/10.1029/2010JG001469, 2011. a
Gerbig, C., Lin, J. C., Wofsy, S. C., Daube, B. C., Andrews, A. E., Stephens,
B. B., Bakwin, P. S., and Grainger, C. A.: Toward constraining regional-scale
fluxes of CO2 with atmospheric observations over a continent: 1. Observed
spatial variability from airborne platforms, J. Geophys. Res, 108, 4756, https://doi.org/10.1029/2002JD003018, 2003. a, b
Gerbig, C., Lin, J. C., Munger, J. W., and Wofsy, S. C.: What can tracer
observations in the continental boundary layer tell us about
surface-atmosphere fluxes?, Atmos. Chem. Phys., 6, 539–554,
https://doi.org/10.5194/acp-6-539-2006, 2006. 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, b, c
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, b
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, https://doi.org/10.1029/2010JD014443, 2010. a, b
Gourdji, S. M., Mueller, K. L., Yadav, V., Huntzinger, D. N., Andrews, A. E.,
Trudeau, M., Petron, G., Nehrkorn, T., Eluszkiewicz, J., Henderson, J., Wen,
D., Lin, J., Fischer, M., Sweeney, C., and Michalak, A. M.: North American
CO2 exchange: inter-comparison of modeled estimates with results from a
fine-scale atmospheric inversion, Biogeosciences, 9, 457–475,
https://doi.org/10.5194/bg-9-457-2012, 2012. a, b
Grell, G. and Devenyi, D.: A generalized approach to parameterizing convection
combining ensemble and data assimilation techniques, Geophys. Res. Lett.,
29, https://doi.org/10.1029/2002GL015311, 2002. a
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic
convective parameterization for weather and air quality modeling, Atmos.
Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014. a, b, c
Grell, G. A., Knoche, R., Peckham, S. E., and McKeen, S. A.: Online versus
offline air quality modeling on cloud-resolving scales, Geophys. Res. Lett.,
31, L16117, https://doi.org/10.1029/2004GL020175 2004. a, b
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock,
W. C., and Eder, B.: Fully coupled online chemistry within the WRF model,
Atmos. Environ., 39, 6957–6975, 2005. a
Guerrette, J. J. and Henze, D. K.: Development and application of the
WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system,
Geosci. Model Dev., 8, 1857–1876, https://doi.org/10.5194/gmd-8-1857-2015,
2015. a
Guerrette, J. J. and Henze, D. K.: Four-dimensional variational inversion of
black carbon emissions during ARCTAS-CARB with WRFDA-Chem, Atmos. Chem.
Phys., 17, 7605–7633, https://doi.org/10.5194/acp-17-7605-2017, 2017. a
Gupta, S., McNider, R., Trainer, M., Zamora, R., Knupp, K., and Singh, M.:
Nocturnal wind structure and plume growth rates due to inertial
oscillations, J. Appl. Meteorol. Climatol., 36, 1050–1063, 1997. a
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D., Bousquet,
P., Bruhwiler, L., Chen, Y. H., Ciais, P., Fan, S., Fung, I. Y., Gloor, M.,
Heimann, M., Higuchi, K., John, J., Maki, T., Maksyutov, S., Masarie, K.,
Peylin, P., Prather, M., Pak, B. C., Randerson, J., Sarmiento, J., Taguchi,
S., Takahashi, T., and Yuen, C. W.: Towards robust regional estimates of CO2
sources and sinks using atmospheric transport models, Nature, 415, 626–630,
2002. a, b, c
Gurney, K. R., Chen, Y. H., Maki, T., Kawa, S. R., Andrews, A., and Zhu, Z. X.:
Sensitivity of atmospheric CO2 inversions to seasonal and interannual
variations in fossil fuel emissions, J. Geophys. Res., 110, d10308, https://doi.org/10.1029/2004JD005373, 2005. a
Hascoet, L. and Pascual, V.: The Tapenade Automatic Differentiation Tool:
Principles, Model, and Specification, ACM Trans. Math. Software, 39, 20,
https://doi.org/10.1145/2450153.2450158, 2013. a
Hourdin, F., Musat, I., Bony, S., Braconnot, P., Codron, F., Dufresne, J. L.,
Fairhead, L., Filiberti, M. A., Friedlingstein, P., Grandpeix, J. Y.,
Krinner, G., Levan, P., Li, Z. X., and Lott, F.: The LMDZ4 general
circulation model: climate performance and sensitivity to parametrized
physics with emphasis on tropical convection, Climate Dyn., 27, 787–813,
2006. a, b
Houweling, S., Aben, I., Breon, F.-M., Chevallier, F., Deutscher, N.,
Engelen, R., Gerbig, C., Griffith, D., Hungershoefer, K., Macatangay, R.,
Marshall, J., Notholt, J., Peters, W., and Serrar, S.: The importance of
transport model uncertainties for the estimation of CO2 sources and sinks
using satellite measurements, Atmos. Chem. Phys., 10, 9981–9992,
https://doi.org/10.5194/acp-10-9981-2010, 2010. 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
Huang, X.-Y., Xiao, Q., Barker, D. M., Zhang, X., Michalakes, J., Huang, W.,
Henderson, T., Bray, J., Chen, Y., Ma, Z., Dudhia, J., Guo, Y., Zhang, X.,
Won, D.-J., Lin, H.-C., and Kuo, Y.-H.: Four-Dimensional Variational Data
Assimilation for WRF: Formulation and Preliminary Results, Mon. Weather Rev.,
137, 299–314, 2009. a, b
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.,
D13103, https://doi.org/10.1029/2008JD009944, 113, 2008. a
Jiang, X., Li, Q. B., Liang, M. C., Shia, R. L., Chahine, M. T., Olsen, E. T.,
Chen, L. L., and Yung, Y. L.: Simulation of upper tropospheric CO2 from
chemistry and transport models, Global Biogeochem. Cy., 22, gB4025, https://doi.org/10.1029/2007GB003049, 2008. a
Kawa, S. R., Erickson, D. J., Pawson, S., and Zhu, Z.: Global CO2
transport
simulations using meteorological data from the NASA data assimilation system,
J. Geophys. Res., 109, d18312, https://doi.org/10.1029/2004JD004554, 2004. a
Kopacz, M., Jacob, D. J., Henze, D. K., Heald, C. L., Streets, D. G., and
Zhang, Q.: Comparison of adjoint and analytical Bayesian inversion methods
for constraining Asian sources of carbon monoxide using satellite (MOPITT)
measurements of CO columns, J. Geophys. Res., 114, d04305, https://doi.org/10.1029/2007JD009264, 2009. a
Kountouris, P., Gerbig, C., Totsche, K.-U., Dolman, A. J., Meesters, A. G. C.
A., Broquet, G., Maignan, F., Gioli, B., Montagnani, L., and Helfter, C.: An
objective prior error quantification for regional atmospheric inverse
applications, Biogeosciences, 12, 7403–7421,
https://doi.org/10.5194/bg-12-7403-2015, 2015. 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
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, d, e
Lauvaux, T., Uliasz, M., Sarrat, C., Chevallier, F., Bousquet, P., Lac, C.,
Davis, K. J., Ciais, P., Denning, A. S., and Rayner, P. J.: Mesoscale
inversion: first results from the CERES campaign with synthetic data, Atmos.
Chem. Phys., 8, 3459–3471, https://doi.org/10.5194/acp-8-3459-2008, 2008. a, b, c, d, e
Law, R. M., Peters, W., Roedenbeck, C., 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., Patra, P. K., Pieterse, G., Rivier, L., Satoh, M., Serrar, S.,
Taguchi, S., Takigawa, M., Vautard, R., Vermeulen, A. T., and Zhu, Z.:
TransCom model simulations of hourly atmospheric CO2: Experimental overview
and diurnal cycle results for 2002, Global Biogeochem. Cy., 22, gB3009,
https://doi.org/10.1029/2007GB003050, 2008. a
Lin, J. C., Gerbig, C., Wofsy, S. C., Andrews, A. E., Daube, B. C., Davis,
K. J., and Grainger, C. A.: A near-field tool for simulating the upstream
influence of atmospheric observations: The Stochastic Time-Inverted
Lagrangian Transport (STILT) model, J. Geophys. Res., 108, 4493, https://doi.org/10.1029/2002JD003161, 2003. a, b
Liu, J., Bowman, K. W., Lee, M., Henze, D. K., Bousserez, N., Brix, H.,
Collatz, G. J., Menemenlis, D., Ott, L., Pawson, S., Jones, D., and Nassar,
R.: Carbon monitoring system flux estimation and attribution: impact of
ACOS-GOSAT X-CO2 sampling on the inference of terrestrial biospheric sources
and sinks, Tellus B, 66, 22486, https://doi.org/10.3402/tellusb.v66.22486, 2014. a
Luis Morales, J. and Nocedal, J.: Remark on “Algorithm 778: L-BFGS-B: Fortran
Subroutines for Large-Scale Bound Constrained Optimization”, ACM Trans.
Math. Software, 38, 7:1–7:4, 2011. a
Mahadevan, P., Wofsy, S. C., Matross, D. M., Xiao, X. M., Dunn, A. L., Lin,
J. C., Gerbig, C., Munger, J. W., Chow, V. Y., and Gottlieb, E. W.: A
satellite-based biosphere parameterization for net ecosystem CO2 exchange:
Vegetation Photosynthesis and Respiration Model (VPRM), Global Biogeochem.
Cy., 22, gB2005, https://doi.org/10.1029/2006GB002735, 2008. a
Meirink, J. F., Bergamaschi, P., Frankenberg, C., d'Amelio, M. T. S.,
Dlugokencky, E. J., Gatti, L. V., Houweling, S., Miller, J. B., Roeckmann,
T., Villani, M. G., and Krol, M. C.: Four-dimensional variational data
assimilation for inverse modeling of atmospheric methane emissions: Analysis
of SCIAMACHY observations, J. Geophys. Res., 113, d17301, https://doi.org/10.1029/2007JD009740, 2008. a, b, c
Michalak, A. M., Bruhwiler, L., and Tans, P. P.: A geostatistical approach to
surface flux estimation of atmospheric trace gases, J. Geophys. Res., 109,
d14109, https://doi.org/10.1029/2003JD004422, 2004. a
Mlawer, E., Taubman, S., Brown, P., Iacono, M., and Clough, S.: Radiative
transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model
for the longwave, J. Geophys. Res., 102, 16663–16682, 1997. a
Nassar, R., Jones, D. B. A., Suntharalingam, P., Chen, J. M., Andres, R. J.,
Wecht, K. J., Yantosca, R. M., Kulawik, S. S., Bowman, K. W., Worden, J. R.,
Machida, T., and Matsueda, H.: Modeling global atmospheric CO2 with
improved emission inventories and CO2 production from the oxidation of
other carbon species, Geosci. Model Dev., 3, 689–716,
https://doi.org/10.5194/gmd-3-689-2010, 2010 a
Nassar, R., Jones, D. B. A., Kulawik, S. S., Worden, J. R., Bowman, K. W.,
Andres, R. J., Suntharalingam, P., Chen, J. M., Brenninkmeijer, C. A. M.,
Schuck, T. J., Conway, T. J., and Worthy, D. E.: Inverse modeling of CO2
sources and sinks using satellite observations of CO2 from TES and surface
flask measurements, Atmos. Chem. Phys., 11, 6029–6047,
https://doi.org/10.5194/acp-11-6029-2011, 2011. a
Nehrkorn, T., Eluszkiewicz, J., Wofsy, S. C., Lin, J. C., Gerbig, C., Longo,
M., and Freitas, S.: Coupled weather research and forecasting-stochastic
time-inverted lagrangian transport (WRF-STILT) model, Meteorol. Atmos. Phys.,
107, 51–64, 2010. a
Nolte, C. G., Appel, K. W., Kelly, J. T., Bhave, P. V., Fahey, K. M., Collett
Jr., J. L., Zhang, L., and Young, J. O.: Evaluation of the Community
Multiscale Air Quality (CMAQ) model v5.0 against size-resolved measurements
of inorganic particle composition across sites in North America, Geosci.
Model Dev., 8, 2877–2892, https://doi.org/10.5194/gmd-8-2877-2015, 2015. a
Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J.,
Masarie, K., Miller, J. B., Bruhwiler, L. M. P., Petron, G., Hirsch, A. I.,
Worthy, D. E. J., van der Werf, G. R., Randerson, J. T., Wennberg, P. O.,
Krol, M. C., and Tans, P. P.: An atmospheric perspective on North American
carbon dioxide exchange: CarbonTracker, P. Natl. Acad. Sci. USA, 104,
18925–18930, 2007. a, b
Peters, W., Miller, J., Whitaker, J., Denning, A., Hirsch, A., Krol, M.,
Zupanski, D., Bruhwiler, L., and Tans, P.: An ensemble data assimilation
system to estimate CO2 surface fluxes from atmospheric trace gas
observations, J. Geophys. Res., 110, D34304, https://doi.org/10.1029/2005JD006157, 2005. a, b
Peylin, P., Baker, D., Sarmiento, J., Ciais, P., and Bousquet, P.: Influence of
transport uncertainty on annual mean and seasonal inversions of atmospheric
CO2 data, J. Geophys. Res., 107, 4385, https://doi.org/10.1029/2001JD000857, 2002. a
Peylin, P., Rayner, P. J., Bousquet, P., Carouge, C., Hourdin, F., Heinrich,
P., Ciais, P., and AEROCARB contributors: Daily CO2 flux estimates over
Europe from continuous atmospheric measurements: 1, inverse methodology,
Atmos. Chem. Phys., 5, 3173–3186, https://doi.org/10.5194/acp-5-3173-2005,
2005. a
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki,
T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van
der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget:
results from an ensemble of atmospheric CO2 inversions, Biogeosciences,
10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, 2013. 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
Pillai, D., Buchwitz, M., Gerbig, C., Koch, T., Reuter, M., Bovensmann, H.,
Marshall, J., and Burrows, J. P.: Tracking city CO2 emissions from space
using a high-resolution inverse modelling approach: a case study for Berlin,
Germany, Atmos. Chem. Phys., 16, 9591–9610,
https://doi.org/10.5194/acp-16-9591-2016, 2016. a
Pleim, J. and Chang, J.: A nonlocal closure model for vertical mixing in the
convective boundary layer, Atmos. Environ., 26, 965–981, 1992. a
Pleim, J. E.: A simple, efficient solution of flux-profile relationships in the
atmospheric surface layer, J. Appl. Meteorol. Climatol., 45, 341–347, 2006. a
Pleim, J. E.: A combined local and nonlocal closure model for the atmospheric
boundary layer. Part II: Application and evaluation in a mesoscale
meteorological model, J. Appl. Meteorol. Climatol., 46, 1396–1409,
2007. a
Pleim, J. E. and Xiu, A. J.: Development of a land surface model. Part II: Data
assimilation, J. Appl. Meteorol. Climatol., 42, 1811–1822, 2003. a
Rabier, F., Jarvinen, H., Klinker, E., Mahfouf, J. F., and Simmons, A.: The
ECMWF operational implementation of four-dimensional variational
assimilation. I: Experimental results with simplified physics, Q. J. Roy.
Meteorol. Soc., 126, 1143–1170, 2000. a
Rayner, P., Enting, I., Francey, R., and Langenfelds, R.: Reconstructing the
recent carbon cycle from atmospheric CO2, delta C-13 and O-2/N-2
observations, Tellus B, 51, 213–232, 1999. a
Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: CO2 flux
history 1982–2001 inferred from atmospheric data using a global inversion of
atmospheric transport, Atmos. Chem. Phys., 3, 1919–1964,
https://doi.org/10.5194/acp-3-1919-2003, 2003. a
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D.,
Hou, Y.-T., Chuang, H.-Y., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez,
M. P., Van Den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The
NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208, 2014. a
Saito, R., Houweling, S., Patra, P. K., Belikov, D., Lokupitiya, R., Niwa, Y.,
Chevallier, F., Saeki, T., and Maksyutov, S.: TransCom satellite
intercomparison experiment: Construction of a bias corrected atmospheric
CO2 climatology, J. Geophys. Res., 116, d21120, https://doi.org/10.1029/2011JD016033, 2011. a
Schuh, A. E., Denning, A. S., Corbin, K. D., Baker, I. T., Uliasz, M.,
Parazoo, N., Andrews, A. E., and Worthy, D. E. J.: A regional high-resolution
carbon flux inversion of North America for 2004, Biogeosciences, 7,
1625–1644, https://doi.org/10.5194/bg-7-1625-2010, 2010. a
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Duda, M., Huang,
X., Wang, W., and Powers, J.: A description of the Advanced Research WRF
version 3, NCAR Tech Note NCAR/TN-475+STR, https://doi.org/10.5065/D68S4MVH 2008.
a, b
Smith, A., Lott, N., and Vose, R.: The Integrated Surface Database Recent
Developments and Partnerships, B. Am. Meteorol. Soc., 92, 704–708,
2011. a
Stephens, B. B., Gurney, K. R., Tans, P. P., Sweeney, C., Peters, W.,
Bruhwiler, L., Ciais, P., Ramonet, M., Bousquet, P., Nakazawa, T., Aoki, S.,
Machida, T., Inoue, G., Vinnichenko, N., Lloyd, J., Jordan, A., Heimann, M.,
Shibistova, O., Langenfelds, R. L., Steele, L. P., Francey, R. J., and
Denning, A. S.: Weak northern and strong tropical land carbon uptake from
vertical profiles of atmospheric CO2, Science, 316, 1732–1735, 2007. a
Stohl, A., Forster, C., Frank, A., Seibert, P., and Wotawa, G.: Technical
note: The Lagrangian particle dispersion model FLEXPART version 6.2, Atmos.
Chem. Phys., 5, 2461–2474, https://doi.org/10.5194/acp-5-2461-2005, 2005. a, b
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
forecasts of winter precipitation using an improved bulk microphysics scheme.
Part II: implementation of a new snow parameterization, Mon. Weather Rev.,
136, 5095–5115, 2008. a
Turner, A. J. and Jacob, D. J.: Balancing aggregation and smoothing errors in
inverse models, Atmos. Chem. Phys., 15, 7039–7048,
https://doi.org/10.5194/acp-15-7039-2015, 2015. a
Yadav, V. and Michalak, A. M.: Improving computational efficiency in large
linear inverse problems: an example from carbon dioxide flux estimation,
Geosci. Model Dev., 6, 583–590, https://doi.org/10.5194/gmd-6-583-2013,
2013. a
Zheng, T., French, N., and Baxter, M.: WRF-CO2 4DVar assimilation system
v1.0, https://doi.org/10.5281/zenodo.1220407, last access: 27 April 2018.
Zhu, C., Byrd, R., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B: Fortran
subroutines for large-scale bound-constrained optimization, ACM Trans.
Math. Software, 23, 550–560, 1997. a
Short summary
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.
We developed WRF-CO2 4D-Var, a carbon dioxide data assimilation system based on the online...
