Articles | Volume 6, issue 1
https://doi.org/10.5194/gmd-6-81-2013
© Author(s) 2013. 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-6-81-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
25 Jan 2013
Model description paper |
| 25 Jan 2013
Global high-resolution simulations of CO2 and CH4 using a NIES transport model to produce a priori concentrations for use in satellite data retrievals
Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan
R. Saito
Research Institute for Global Change, JAMSTEC, Yokohama, 236-0001, Japan
D. Belikov
Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan
Division for Polar Research, National Institute of Polar Research, Tachikawa, Tokyo 190-8518, Japan
S. Maksyutov
Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, 305-8506, Japan
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Shamil Maksyutov, Tomohiro Oda, Makoto Saito, Rajesh Janardanan, Dmitry Belikov, Johannes W. Kaiser, Ruslan Zhuravlev, Alexander Ganshin, Vinu K. Valsala, Arlyn Andrews, Lukasz Chmura, Edward Dlugokencky, László Haszpra, Ray L. Langenfelds, Toshinobu Machida, Takakiyo Nakazawa, Michel Ramonet, Colm Sweeney, and Douglas Worthy
Atmos. Chem. Phys., 21, 1245–1266, https://doi.org/10.5194/acp-21-1245-2021, https://doi.org/10.5194/acp-21-1245-2021, 2021
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Dmitry A. Belikov, Naoko Saitoh, Prabir K. Patra, and Naveen Chandra
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Atmos. Chem. Phys., 19, 13017–13035, https://doi.org/10.5194/acp-19-13017-2019, https://doi.org/10.5194/acp-19-13017-2019, 2019
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Misa Ishizawa, Douglas Chan, Doug Worthy, Elton Chan, Felix Vogel, and Shamil Maksyutov
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Benjamin Gaubert, Britton B. Stephens, Sourish Basu, Frédéric Chevallier, Feng Deng, Eric A. Kort, Prabir K. Patra, Wouter Peters, Christian Rödenbeck, Tazu Saeki, David Schimel, Ingrid Van der Laan-Luijkx, Steven Wofsy, and Yi Yin
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We have compared global carbon budgets calculated from numerical inverse models and CO2 observations, and evaluated how these systems reproduce vertical gradients in atmospheric CO2 from aircraft measurements. We found that available models have converged on near-neutral tropical total fluxes for several decades, implying consistent sinks in intact tropical forests, and that assumed fossil fuel emissions and predicted atmospheric growth rates are now the dominant axes of disagreement.
Jacob K. Hedelius, Junjie Liu, Tomohiro Oda, Shamil Maksyutov, Coleen M. Roehl, Laura T. Iraci, James R. Podolske, Patrick W. Hillyard, Jianming Liang, Kevin R. Gurney, Debra Wunch, and Paul O. Wennberg
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Maarten Krol, Marco de Bruine, Lars Killaars, Huug Ouwersloot, Andrea Pozzer, Yi Yin, Frederic Chevallier, Philippe Bousquet, Prabir Patra, Dmitry Belikov, Shamil Maksyutov, Sandip Dhomse, Wuhu Feng, and Martyn P. Chipperfield
Geosci. Model Dev., 11, 3109–3130, https://doi.org/10.5194/gmd-11-3109-2018, https://doi.org/10.5194/gmd-11-3109-2018, 2018
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Janne Hakkarainen, Iolanda Ialongo, Shamil Maksyutov, and David Crisp
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-649, https://doi.org/10.5194/acp-2018-649, 2018
Revised manuscript not accepted
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We provide a global (60° S–60° N) view of the XCO2 anomalies, indicators of CO2 emissions to and removal from the atmosphere, and study their annual variations and seasonal patterns. We see that positive anomalies correspond to the emissions from fossil fuel combustion over the major industrial areas as well as biomass burning during different fire seasons. The largest negative anomalies correspond to the growing seasons in the middle latitudes. The results are achieved using NASA's OCO-2 data.
Yang Qu, Shamil Maksyutov, and Qianlai Zhuang
Biogeosciences, 15, 3967–3973, https://doi.org/10.5194/bg-15-3967-2018, https://doi.org/10.5194/bg-15-3967-2018, 2018
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We developed an algorithm for a fast spin-up by finding the exact solution of a linearized system representing the cyclo-stationary state of a model and implemented it in a biogeochemistry model, the Terrestrial Ecosystem Model. For the test sites with five different plant functional types, the new method saves over 90 % of the original spin-up time in site-level simulations. The developed spin-up method will be used for future quantification of carbon dynamics at fine spatiotemporal scales.
Tomohiro Oda, Shamil Maksyutov, and Robert J. Andres
Earth Syst. Sci. Data, 10, 87–107, https://doi.org/10.5194/essd-10-87-2018, https://doi.org/10.5194/essd-10-87-2018, 2018
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The Open-source Data Inventory for Anthropogenic CO2 (ODIAC) is a 1 x 1 km global high-resolution fossil fuel CO2 emissions data product. ODIAC first introduced the combined use of point source profiles and nighttime light satellite data to create high-resolution emissions spatial distributions and it has been intensively used in the carbon cycle research community. This manuscript describes the 2016 version of ODIAC data, the modeling approach, and future data production and model developments.
Naveen Chandra, Sachiko Hayashida, Tazu Saeki, and Prabir K. Patra
Atmos. Chem. Phys., 17, 12633–12643, https://doi.org/10.5194/acp-17-12633-2017, https://doi.org/10.5194/acp-17-12633-2017, 2017
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This study shows difficulties in interpreting columnar dry-air mole fractions of methane (XCH4) for surface emissions of CH4 over the South Asia region, without separating the role of chemistry and transport. Using a chemistry-transport model, we suggest that a link between surface emissions and higher levels of XCH4 is not always valid in this region of complex monsoonal meteorology, although there is often a fair correlation between the seasonal variations in surface emissions and XCH4.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
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Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Aleksandr F. Sabrekov, Benjamin R. K. Runkle, Mikhail V. Glagolev, Irina E. Terentieva, Victor M. Stepanenko, Oleg R. Kotsyurbenko, Shamil S. Maksyutov, and Oleg S. Pokrovsky
Biogeosciences, 14, 3715–3742, https://doi.org/10.5194/bg-14-3715-2017, https://doi.org/10.5194/bg-14-3715-2017, 2017
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Dmitry A. Belikov, Shamil Maksyutov, Alexander Ganshin, Ruslan Zhuravlev, Nicholas M. Deutscher, Debra Wunch, Dietrich G. Feist, Isamu Morino, Robert J. Parker, Kimberly Strong, Yukio Yoshida, Andrey Bril, Sergey Oshchepkov, Hartmut Boesch, Manvendra K. Dubey, David Griffith, Will Hewson, Rigel Kivi, Joseph Mendonca, Justus Notholt, Matthias Schneider, Ralf Sussmann, Voltaire A. Velazco, and Shuji Aoki
Atmos. Chem. Phys., 17, 143–157, https://doi.org/10.5194/acp-17-143-2017, https://doi.org/10.5194/acp-17-143-2017, 2017
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
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An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Irina Evgenievna Terentieva, Mikhail Vladimirovich Glagolev, Elena Dmitrievna Lapshina, Alexandr Faritovich Sabrekov, and Shamil Maksyutov
Biogeosciences, 13, 4615–4626, https://doi.org/10.5194/bg-13-4615-2016, https://doi.org/10.5194/bg-13-4615-2016, 2016
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West Siberia (WS) wetlands are the world’s largest high-latitude wetland system. WS methane emission estimates suffered from large uncertainty due to high emission rate variability across the wetland vegetation cover. We mapped WS taiga zone wetlands with Landsat imagery and applied wetland typology specifically developed to reflect heterogeneity of methane fluxes. The map provides a benchmark for validation of coarse-resolution land cover products and wetland data sets in high latitudes.
Misa Ishizawa, Osamu Uchino, Isamu Morino, Makoto Inoue, Yukio Yoshida, Kazuo Mabuchi, Tomoko Shirai, Yasunori Tohjima, Shamil Maksyutov, Hirofumi Ohyama, Shuji Kawakami, Atsushi Takizawa, and Dmitry Belikov
Atmos. Chem. Phys., 16, 9149–9161, https://doi.org/10.5194/acp-16-9149-2016, https://doi.org/10.5194/acp-16-9149-2016, 2016
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Greenhouse gases Observing SATellite (GOSAT) was launched to monitor CO2 and CH4 concentrations from the space. This paper analyses an extremely high XCH4 event over Northeast Asia observed by GOSAT in the summer of 2013. Results indicate that the high XCH4 event was caused by fast transport of CH4-rich air from East China to Japan due to anomalies of north Pacific high-pressure system over East Asia. This study demonstrates the capability of GOSAT to detect an XCH4 event on a synoptic scale.
R. Hossaini, P. K. Patra, A. A. Leeson, G. Krysztofiak, N. L. Abraham, S. J. Andrews, A. T. Archibald, J. Aschmann, E. L. Atlas, D. A. Belikov, H. Bönisch, L. J. Carpenter, S. Dhomse, M. Dorf, A. Engel, W. Feng, S. Fuhlbrügge, P. T. Griffiths, N. R. P. Harris, R. Hommel, T. Keber, K. Krüger, S. T. Lennartz, S. Maksyutov, H. Mantle, G. P. Mills, B. Miller, S. A. Montzka, F. Moore, M. A. Navarro, D. E. Oram, K. Pfeilsticker, J. A. Pyle, B. Quack, A. D. Robinson, E. Saikawa, A. Saiz-Lopez, S. Sala, B.-M. Sinnhuber, S. Taguchi, S. Tegtmeier, R. T. Lidster, C. Wilson, and F. Ziska
Atmos. Chem. Phys., 16, 9163–9187, https://doi.org/10.5194/acp-16-9163-2016, https://doi.org/10.5194/acp-16-9163-2016, 2016
Dmitry A. Belikov, Shamil Maksyutov, Alexey Yaremchuk, Alexander Ganshin, Thomas Kaminski, Simon Blessing, Motoki Sasakawa, Angel J. Gomez-Pelaez, and Alexander Starchenko
Geosci. Model Dev., 9, 749–764, https://doi.org/10.5194/gmd-9-749-2016, https://doi.org/10.5194/gmd-9-749-2016, 2016
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
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We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
A. Ghosh, P. K. Patra, K. Ishijima, T. Umezawa, A. Ito, D. M. Etheridge, S. Sugawara, K. Kawamura, J. B. Miller, E. J. Dlugokencky, P. B. Krummel, P. J. Fraser, L. P. Steele, R. L. Langenfelds, C. M. Trudinger, J. W. C. White, B. Vaughn, T. Saeki, S. Aoki, and T. Nakazawa
Atmos. Chem. Phys., 15, 2595–2612, https://doi.org/10.5194/acp-15-2595-2015, https://doi.org/10.5194/acp-15-2595-2015, 2015
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Atmospheric CH4 increased from 900ppb to 1800ppb during the period 1900–2010 at a rate unprecedented in any observational records. We use bottom-up emissions and a chemistry-transport model to simulate CH4. The optimized global total CH4 emission, estimated from the model–observation differences, increased at fastest rate during 1940–1990. Using δ13C of CH4 measurements we attribute this emission increase to biomass burning. Total CH4 lifetime is shortened by 4% over the simulation period.
C. Song, S. Maksyutov, D. Belikov, H. Takagi, and J. Shu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-6745-2015, https://doi.org/10.5194/acpd-15-6745-2015, 2015
Preprint withdrawn
M. Inoue, I. Morino, O. Uchino, Y. Miyamoto, T. Saeki, Y. Yoshida, T. Yokota, C. Sweeney, P. P. Tans, S. C. Biraud, T. Machida, J. V. Pittman, E. A. Kort, T. Tanaka, S. Kawakami, Y. Sawa, K. Tsuboi, and H. Matsueda
Atmos. Meas. Tech., 7, 2987–3005, https://doi.org/10.5194/amt-7-2987-2014, https://doi.org/10.5194/amt-7-2987-2014, 2014
M. Saito, A. Ito, and S. Maksyutov
Geosci. Model Dev., 7, 1829–1840, https://doi.org/10.5194/gmd-7-1829-2014, https://doi.org/10.5194/gmd-7-1829-2014, 2014
Y. Tohjima, M. Kubo, C. Minejima, H. Mukai, H. Tanimoto, A. Ganshin, S. Maksyutov, K. Katsumata, T. Machida, and K. Kita
Atmos. Chem. Phys., 14, 1663–1677, https://doi.org/10.5194/acp-14-1663-2014, https://doi.org/10.5194/acp-14-1663-2014, 2014
T. J. Bohn, E. Podest, R. Schroeder, N. Pinto, K. C. McDonald, M. Glagolev, I. Filippov, S. Maksyutov, M. Heimann, X. Chen, and D. P. Lettenmaier
Biogeosciences, 10, 6559–6576, https://doi.org/10.5194/bg-10-6559-2013, https://doi.org/10.5194/bg-10-6559-2013, 2013
R. Locatelli, P. Bousquet, F. Chevallier, A. Fortems-Cheney, S. Szopa, M. Saunois, A. Agusti-Panareda, D. Bergmann, H. Bian, P. Cameron-Smith, M. P. Chipperfield, E. Gloor, S. Houweling, S. R. Kawa, M. Krol, P. K. Patra, R. G. Prinn, M. Rigby, R. Saito, and C. Wilson
Atmos. Chem. Phys., 13, 9917–9937, https://doi.org/10.5194/acp-13-9917-2013, https://doi.org/10.5194/acp-13-9917-2013, 2013
S. Maksyutov, H. Takagi, V. K. Valsala, M. Saito, T. Oda, T. Saeki, D. A. Belikov, R. Saito, A. Ito, Y. Yoshida, I. Morino, O. Uchino, R. J. Andres, and T. Yokota
Atmos. Chem. Phys., 13, 9351–9373, https://doi.org/10.5194/acp-13-9351-2013, https://doi.org/10.5194/acp-13-9351-2013, 2013
Y. Yoshida, N. Kikuchi, I. Morino, O. Uchino, S. Oshchepkov, A. Bril, T. Saeki, N. Schutgens, G. C. Toon, D. Wunch, C. M. Roehl, P. O. Wennberg, D. W. T. Griffith, N. M. Deutscher, T. Warneke, J. Notholt, J. Robinson, V. Sherlock, B. Connor, M. Rettinger, R. Sussmann, P. Ahonen, P. Heikkinen, E. Kyrö, J. Mendonca, K. Strong, F. Hase, S. Dohe, and T. Yokota
Atmos. Meas. Tech., 6, 1533–1547, https://doi.org/10.5194/amt-6-1533-2013, https://doi.org/10.5194/amt-6-1533-2013, 2013
D. A. Belikov, S. Maksyutov, M. Krol, A. Fraser, M. Rigby, H. Bian, A. Agusti-Panareda, D. Bergmann, P. Bousquet, P. Cameron-Smith, M. P. Chipperfield, A. Fortems-Cheiney, E. Gloor, K. Haynes, P. Hess, S. Houweling, S. R. Kawa, R. M. Law, Z. Loh, L. Meng, P. I. Palmer, P. K. Patra, R. G. Prinn, R. Saito, and C. Wilson
Atmos. Chem. Phys., 13, 1093–1114, https://doi.org/10.5194/acp-13-1093-2013, https://doi.org/10.5194/acp-13-1093-2013, 2013
Related subject area
Atmospheric sciences
Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas
An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models
Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion
Improved representation of volcanic sulfur dioxide depletion in Lagrangian transport simulations: a case study with MPTRAC v2.4
Use of threshold parameter variation for tropical cyclone tracking
Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes
The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale
A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter
Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1
CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
A method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)
Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis
Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)
Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance
A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0
The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models
The development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)
GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)
Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models
Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method
An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters
Sensitivity of tropospheric ozone to halogen chemistry in the chemistry–climate model LMDZ-INCA vNMHC
Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants
Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models
The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications
An approach to refining the ground meteorological observation stations for improving PM2.5 forecasts in the Beijing–Tianjin–Hebei region
Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS
Rapid adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0
Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm
Self-nested large-eddy simulations in PALM model system v21.10 for offshore wind prediction under different atmospheric stability conditions
How does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00?
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
Updated isoprene and terpene emission factors for the Interactive BVOC (iBVOC) emission scheme in the United Kingdom Earth System Model (UKESM1.0)
Technical descriptions of the experimental dynamical downscaling simulations over North America by the CAM–MPAS variable-resolution model
Intercomparison of the weather and climate physics suites of a unified forecast–climate model system (GRIST-A22.7.28) based on single-column modeling
A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, Variational Mode Decomposition, Principal Component Analysis, and Random Forest: VMD-PCA-RF (version 1.0.0)
Halogen chemistry in volcanic plumes: a 1D framework based on MOCAGE 1D (version R1.18.1) preparing 3D global chemistry modelling
PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis
CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting
Long-term evaluation of surface air pollution in CAMSRA and MERRA-2 global reanalyses over Europe (2003–2020)
A simplified non-linear chemistry-transport model for analyzing NO2 column observations
Evaluating Three Decades of Precipitation in the Upper Colorado River Basin from a High-Resolution Regional Climate Model
Emulating aerosol optics with randomly generated neural networks
Development of an ecophysiology module in the GEOS-Chem chemical transport model version 12.2.0 to represent biosphere–atmosphere fluxes relevant for ozone air quality
Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality in Africa
Comparison of ozone formation attribution techniques in the northeastern United States
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
Geosci. Model Dev., 16, 5493–5514, https://doi.org/10.5194/gmd-16-5493-2023, https://doi.org/10.5194/gmd-16-5493-2023, 2023
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With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023, https://doi.org/10.5194/gmd-16-5323-2023, 2023
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Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidentally) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011) and show that it improves the simulation of wet deposition.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023, https://doi.org/10.5194/gmd-16-5281-2023, 2023
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A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023, https://doi.org/10.5194/gmd-16-5251-2023, 2023
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In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
Daniel Yazgi and Tinja Olenius
Geosci. Model Dev., 16, 5237–5249, https://doi.org/10.5194/gmd-16-5237-2023, https://doi.org/10.5194/gmd-16-5237-2023, 2023
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We present flexible tools to implement aerosol formation rate predictions in climate and chemical transport models. New-particle formation is a significant but uncertain factor affecting aerosol numbers and an active field within molecular modeling which provides data for assessing formation rates for different chemical species. We introduce tools to generate and interpolate formation rate lookup tables for user-defined data, thus enabling the easy inclusion and testing of formation schemes.
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023, https://doi.org/10.5194/gmd-16-5219-2023, 2023
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Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.
Mingzhao Liu, Lars Hoffmann, Sabine Griessbach, Zhongyin Cai, Yi Heng, and Xue Wu
Geosci. Model Dev., 16, 5197–5217, https://doi.org/10.5194/gmd-16-5197-2023, https://doi.org/10.5194/gmd-16-5197-2023, 2023
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We introduce new and revised chemistry and physics modules in the Massive-Parallel Trajectory Calculations (MPTRAC) Lagrangian transport model aiming to improve the representation of volcanic SO2 transport and depletion. We test these modules in a case study of the Ambae eruption in July 2018 in which the SO2 plume underwent wet removal and convection. The lifetime of SO2 shows highly variable and complex dependencies on the atmospheric conditions at different release heights.
Bernhard M. Enz, Jan P. Engelmann, and Ulrike Lohmann
Geosci. Model Dev., 16, 5093–5112, https://doi.org/10.5194/gmd-16-5093-2023, https://doi.org/10.5194/gmd-16-5093-2023, 2023
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An algorithm to track tropical cyclones in model simulation data has been developed. The algorithm uses many combinations of varying parameter thresholds to detect weaker phases of tropical cyclones while still being resilient to false positives. It is shown that the algorithm performs well and adequately represents the tropical cyclone activity of the underlying simulation data. The impact of false positives on overall tropical cyclone activity is shown to be insignificant.
Sepehr Fathi, Mark Gordon, and Yongsheng Chen
Geosci. Model Dev., 16, 5069–5091, https://doi.org/10.5194/gmd-16-5069-2023, https://doi.org/10.5194/gmd-16-5069-2023, 2023
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We have combined various capabilities within a WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The study objective was to resolve transport processes at the scale of measurements to assess and optimize aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological and within 1–2 standard deviations of observed wind fields. Mass was conserved in the model within 5 % of input emissions.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
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The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
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Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
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A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
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We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Yosuke Yamazaki
Geosci. Model Dev., 16, 4749–4766, https://doi.org/10.5194/gmd-16-4749-2023, https://doi.org/10.5194/gmd-16-4749-2023, 2023
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The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
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To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev., 16, 4617–4638, https://doi.org/10.5194/gmd-16-4617-2023, https://doi.org/10.5194/gmd-16-4617-2023, 2023
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Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, https://doi.org/10.5194/gmd-16-4551-2023, 2023
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The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev., 16, 4405–4425, https://doi.org/10.5194/gmd-16-4405-2023, https://doi.org/10.5194/gmd-16-4405-2023, 2023
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Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, https://doi.org/10.5194/gmd-16-4427-2023, 2023
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We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403, https://doi.org/10.5194/gmd-16-4385-2023, https://doi.org/10.5194/gmd-16-4385-2023, 2023
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The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383, https://doi.org/10.5194/gmd-16-4367-2023, https://doi.org/10.5194/gmd-16-4367-2023, 2023
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Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Laurent Menut
Geosci. Model Dev., 16, 4265–4281, https://doi.org/10.5194/gmd-16-4265-2023, https://doi.org/10.5194/gmd-16-4265-2023, 2023
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This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211, https://doi.org/10.5194/gmd-16-4193-2023, https://doi.org/10.5194/gmd-16-4193-2023, 2023
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In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
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The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062, https://doi.org/10.5194/gmd-16-4041-2023, https://doi.org/10.5194/gmd-16-4041-2023, 2023
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We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
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Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
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We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, https://doi.org/10.5194/gmd-16-3873-2023, 2023
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The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Lichao Yang, Wansuo Duan, and Zifa Wang
Geosci. Model Dev., 16, 3827–3848, https://doi.org/10.5194/gmd-16-3827-2023, https://doi.org/10.5194/gmd-16-3827-2023, 2023
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An approach is proposed to refine a ground meteorological observation network to improve the PM2.5 forecasts in the Beijing–Tianjin–Hebei region. A cost-effective observation network is obtained and makes the relevant PM2.5 forecasts assimilate fewer observations but achieve the forecasting skill comparable to or higher than that obtained by assimilating all ground station observations, suggesting that many of the current ground stations can be greatly scattered to avoid much unnecessary work.
Abhishekh Kumar Srivastava, Paul Aaron Ullrich, Deeksha Rastogi, Pouya Vahmani, Andrew Jones, and Richard Grotjahn
Geosci. Model Dev., 16, 3699–3722, https://doi.org/10.5194/gmd-16-3699-2023, https://doi.org/10.5194/gmd-16-3699-2023, 2023
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Stakeholders need high-resolution regional climate data for applications such as assessing water availability and mountain snowpack. This study examines 3 h and 24 h historical precipitation over the contiguous United States in the 12 km WRF version 4.2.1-based dynamical downscaling of the ERA5 reanalysis. WRF improves precipitation characteristics such as the annual cycle and distribution of the precipitation maxima, but it also displays regionally and seasonally varying precipitation biases.
Jiangyong Li, Chunlin Zhang, Wenlong Zhao, Shijie Han, Yu Wang, Hao Wang, and Boguang Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-90, https://doi.org/10.5194/gmd-2023-90, 2023
Revised manuscript accepted for GMD
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Photochemical box model is a critical tool to understand the chemistry in troposphere, but its application is hampered by the slow computation efficiency in solving the massive chemical equations. The ROMAC model developed in this study integrated a more efficient atmospheric chemistry solver and an adaptive optimization algorithm, which can improve the computational efficiency up to 96 % and also overcome the shortcomings of physical modules being oversimplified in the traditional box models.
Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao
Geosci. Model Dev., 16, 3611–3628, https://doi.org/10.5194/gmd-16-3611-2023, https://doi.org/10.5194/gmd-16-3611-2023, 2023
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Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Geosci. Model Dev., 16, 3553–3564, https://doi.org/10.5194/gmd-16-3553-2023, https://doi.org/10.5194/gmd-16-3553-2023, 2023
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Local refinement of the grid is a powerful method allowing us to reduce the computational time while preserving the accuracy in the area of interest. Depending on the implementation, the local refinement may introduce unwanted numerical effects into the results. We study the wind speed common to the wind turbine operational speeds and confirm strong alteration of the result when the heat fluxes are present, except for the specific refinement scheme used.
Sylvia Sullivan, Behrooz Keshtgar, Nicole Albern, Elzina Bala, Christoph Braun, Anubhav Choudhary, Johannes Hörner, Hilke Lentink, Georgios Papavasileiou, and Aiko Voigt
Geosci. Model Dev., 16, 3535–3551, https://doi.org/10.5194/gmd-16-3535-2023, https://doi.org/10.5194/gmd-16-3535-2023, 2023
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Clouds absorb and re-emit infrared radiation from Earth's surface and absorb and reflect incoming solar radiation. As a result, they change atmospheric temperature gradients that drive large-scale circulation. To better simulate this circulation, we study how the radiative heating and cooling from clouds depends on model settings like grid spacing; whether we describe convection approximately or exactly; and the level of detail used to describe small-scale processes, or microphysics, in clouds.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavcic, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben J. Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
EGUsphere, https://doi.org/10.5194/egusphere-2023-647, https://doi.org/10.5194/egusphere-2023-647, 2023
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3D climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
James Weber, James A. King, Katerina Sindelarova, and Maria Val Martin
Geosci. Model Dev., 16, 3083–3101, https://doi.org/10.5194/gmd-16-3083-2023, https://doi.org/10.5194/gmd-16-3083-2023, 2023
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The emissions of volatile organic compounds from vegetation (BVOCs) influence atmospheric composition and contribute to certain gases and aerosols (tiny airborne particles) which play a role in climate change. BVOC emissions are likely to change in the future due to changes in climate and land use. Therefore, accurate simulation of BVOC emission is important, and this study describes an update to the simulation of BVOC emissions in the United Kingdom Earth System Model (UKESM).
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
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We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Xiaohan Li, Yi Zhang, Xindong Peng, Baiquan Zhou, Jian Li, and Yiming Wang
Geosci. Model Dev., 16, 2975–2993, https://doi.org/10.5194/gmd-16-2975-2023, https://doi.org/10.5194/gmd-16-2975-2023, 2023
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The weather and climate physics suites used in GRIST-A22.7.28 are compared using single-column modeling. The source of their discrepancies in terms of modeling cloud and precipitation is explored. Convective parameterization is found to be a key factor responsible for the differences. The two suites also have intrinsic differences in the interaction between microphysics and other processes, resulting in different cloud features and time step sensitivities.
Owen Kenneth Hughes and Christiane Jablonowski
EGUsphere, https://doi.org/10.5194/egusphere-2023-376, https://doi.org/10.5194/egusphere-2023-376, 2023
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Atmospheric models benefit from idealized tests that assess their accuracy in a simpler simulation. A new test with artificial mountains is developed for models on a spherical earth. The mountains trigger the development of both planetary-scale and small-scale waves. These can be analyzed in dry or moist environments with a simple rainfall mechanism. Four atmospheric models are intercompared. This sheds light on the pros and cons of the model designs and the impact of mountains on the flow.
Shaohui Zhou, Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
EGUsphere, https://doi.org/10.5194/egusphere-2023-945, https://doi.org/10.5194/egusphere-2023-945, 2023
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The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indexes for 10 months remain relatively stable: accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
Virginie Marécal, Ronan Voisin-Plessis, Tjarda Jane Roberts, Alessandro Aiuppa, Herizo Narivelo, Paul David Hamer, Béatrice Josse, Jonathan Guth, Luke Surl, and Lisa Grellier
Geosci. Model Dev., 16, 2873–2898, https://doi.org/10.5194/gmd-16-2873-2023, https://doi.org/10.5194/gmd-16-2873-2023, 2023
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We implemented a halogen volcanic chemistry scheme in a one-dimensional modelling framework preparing for further use in a three-dimensional global chemistry-transport model. The results of the simulations for an eruption of Mt Etna in 2008, including various sensitivity tests, show a good consistency with previous modelling studies.
Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang
Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, https://doi.org/10.5194/gmd-16-2753-2023, 2023
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PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi
Geosci. Model Dev., 16, 2737–2752, https://doi.org/10.5194/gmd-16-2737-2023, https://doi.org/10.5194/gmd-16-2737-2023, 2023
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Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Aleksander Lacima, Hervé Petetin, Albert Soret, Dene Bowdalo, Oriol Jorba, Zhaoyue Chen, Raúl F. Méndez Turrubiates, Hicham Achebak, Joan Ballester, and Carlos Pérez García-Pando
Geosci. Model Dev., 16, 2689–2718, https://doi.org/10.5194/gmd-16-2689-2023, https://doi.org/10.5194/gmd-16-2689-2023, 2023
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Understanding how air pollution varies across space and time is of key importance for the safeguarding of human health. This work arose in the context of the project EARLY-ADAPT, for which the Barcelona Supercomputing Center developed an air pollution database covering all of Europe. Through different statistical methods, we compared two global pollution models against measurements from ground stations and found significant discrepancies between the observed and the modeled surface pollution.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
EGUsphere, https://doi.org/10.5194/egusphere-2023-876, https://doi.org/10.5194/egusphere-2023-876, 2023
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To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and evaluate modeled results against TROPOMI v2 over multiple power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind direction and prior emissions.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-69, https://doi.org/10.5194/gmd-2023-69, 2023
Revised manuscript accepted for GMD
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It's important to know how well atmospheric models do in the mountains, but there aren't very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado river basin against the data that's available. The model works pretty well but, there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we couldn't before.
Andrew Geiss, Po-Lun Ma, Balwinder Singh, and Joseph C. Hardin
Geosci. Model Dev., 16, 2355–2370, https://doi.org/10.5194/gmd-16-2355-2023, https://doi.org/10.5194/gmd-16-2355-2023, 2023
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Atmospheric aerosols play a critical role in Earth's climate, but it is too computationally expensive to directly model their interaction with radiation in climate simulations. This work develops a new neural-network-based parameterization of aerosol optical properties for use in the Energy Exascale Earth System Model that is much more accurate than the current one; it also introduces a unique model optimization method that involves randomly generating neural network architectures.
Joey C. Y. Lam, Amos P. K. Tai, Jason A. Ducker, and Christopher D. Holmes
Geosci. Model Dev., 16, 2323–2342, https://doi.org/10.5194/gmd-16-2323-2023, https://doi.org/10.5194/gmd-16-2323-2023, 2023
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We developed a new component within an atmospheric chemistry model to better simulate plant ecophysiological processes relevant for ozone air quality. We showed that it reduces simulated biases in plant uptake of ozone in prior models. The new model enables us to explore how future climatic changes affect air quality via affecting plants, examine ozone–vegetation interactions and feedbacks, and evaluate the impacts of changing atmospheric chemistry and climate on vegetation productivity.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-50, https://doi.org/10.5194/gmd-2023-50, 2023
Revised manuscript accepted for GMD
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations, and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model-satellite discrepancies, we find that future field campaigns in an East African region (30° E – 45° E, 5° S – 5° N) could substantially improve the predictive skill of air quality models.
Qian Shu, Sergey L. Napelenok, William T. Hutzell, Kirk R. Baker, Barron H. Henderson, Benjamin N. Murphy, and Christian Hogrefe
Geosci. Model Dev., 16, 2303–2322, https://doi.org/10.5194/gmd-16-2303-2023, https://doi.org/10.5194/gmd-16-2303-2023, 2023
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Source attribution methods are generally used to determine culpability of precursor emission sources to ambient pollutant concentrations. However, source attribution of secondarily formed pollutants such as ozone and its precursors cannot be explicitly measured, making evaluation of source apportionment methods challenging. In this study, multiple apportionment approach comparisons show common features but still reveal wide variations in predicted sector contribution and species dependency.
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