Articles | Volume 14, issue 2
https://doi.org/10.5194/gmd-14-795-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-14-795-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
04 Feb 2021
Model description paper |
| 04 Feb 2021
The Nonhydrostatic ICosahedral Atmospheric Model for CMIP6 HighResMIP simulations (NICAM16-S): experimental design, model description, and impacts of model updates
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Tomoki Ohno
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Tatsuya Seiki
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Hisashi Yashiro
National Institute for Environmental Studies, Tsukuba,
305-8506, Japan
Akira T. Noda
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Masuo Nakano
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Yohei Yamada
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Woosub Roh
Atmosphere and Ocean Research Institute, The University of Tokyo,
Kashiwa, 277-8564, Japan
Masaki Satoh
Atmosphere and Ocean Research Institute, The University of Tokyo,
Kashiwa, 277-8564, Japan
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Tomoko Nitta
Atmosphere and Ocean Research Institute, The University of Tokyo,
Kashiwa, 277-8564, Japan
Daisuke Goto
National Institute for Environmental Studies, Tsukuba,
305-8506, Japan
Hiroaki Miura
Department of Earth and Planetary Science, Graduate School of Science,
The University of Tokyo, Tokyo, 113-0033, Japan
Tomoe Nasuno
Japan Agency for Marine-Earth Science and Technology, Yokohama,
236-0001, Japan
Tomoki Miyakawa
Atmosphere and Ocean Research Institute, The University of Tokyo,
Kashiwa, 277-8564, Japan
Ying-Wen Chen
Atmosphere and Ocean Research Institute, The University of Tokyo,
Kashiwa, 277-8564, Japan
Masato Sugi
Meteorological Research Institute, Tsukuba, 305-0052, Japan
Related authors
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
Short summary
Short summary
The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Reindert J. Haarsma, Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, Susanna Corti, Neven S. Fučkar, Virginie Guemas, Jost von Hardenberg, Wilco Hazeleger, Chihiro Kodama, Torben Koenigk, L. Ruby Leung, Jian Lu, Jing-Jia Luo, Jiafu Mao, Matthew S. Mizielinski, Ryo Mizuta, Paulo Nobre, Masaki Satoh, Enrico Scoccimarro, Tido Semmler, Justin Small, and Jin-Song von Storch
Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, https://doi.org/10.5194/gmd-9-4185-2016, 2016
Short summary
Short summary
Recent progress in computing power has enabled climate models to simulate more processes in detail and on a smaller scale. Here we present a common protocol for these high-resolution runs that will foster the analysis and understanding of the impact of model resolution on the simulated climate. These runs will also serve as a more reliable source for assessing climate risks that are associated with small-scale weather phenomena such as tropical cyclones.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-18, https://doi.org/10.5194/amt-2023-18, 2023
Preprint under review for AMT
Short summary
Short summary
JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM simulation with 3.5 km horizontal resolution, and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023, https://doi.org/10.5194/amt-16-603-2023, 2023
Short summary
Short summary
SMILE (a spectral misalignment in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval products, we did a simulation of EarthCARE-MSI forward radiation, evaluating the error in simulated scenes from a global cloud system-resolving model and a satellite simulator. Our results indicated that the error from SMILE was generally small and negligible for oceanic scenes.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
EGUsphere, https://doi.org/10.5194/egusphere-2022-1255, https://doi.org/10.5194/egusphere-2022-1255, 2022
Short summary
Short summary
We evaluated effectiveness of horizontal integration and unfolding method for the reduction of Doppler velocity error in the Level 2 algorithm of CPR. We used radar reflectivity and Doppler data from a global storm-resolving simulation and a satellite simulator. The Doppler error was higher in the tropics than in the other latitudes because of frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the Doppler errors become small enough.
Maria Paula Pérez-Peña, Jenny A. Fisher, Dylan B. Millet, Hisashi Yashiro, Ray L. Langenfelds, Paul B. Krummel, and Scott H. Kable
Atmos. Chem. Phys., 22, 12367–12386, https://doi.org/10.5194/acp-22-12367-2022, https://doi.org/10.5194/acp-22-12367-2022, 2022
Short summary
Short summary
We used two atmospheric models to test the implications of previously unexplored aldehyde photochemistry on the atmospheric levels of molecular hydrogen (H2). We showed that the new photochemistry from aldehydes produces more H2 over densely forested areas. Compared to the rest of the world, it is over these forested regions where the produced H2 is more likely to be removed. The results highlight that other processes that contribute to atmospheric H2 levels should be studied further.
Tie Dai, Yueming Cheng, Daisuke Goto, Yingruo Li, Xiao Tang, Guangyu Shi, and Teruyuki Nakajima
Atmos. Chem. Phys., 21, 4357–4379, https://doi.org/10.5194/acp-21-4357-2021, https://doi.org/10.5194/acp-21-4357-2021, 2021
Short summary
Short summary
The anthropogenic emission of sulfur dioxide (SO2) over China has significantly declined as a consequence of the clean air actions. We have developed a new emission inversion system to dynamically update the SO2 emission grid by grid over China by assimilating ground-based SO2 observations. The inverted SO2 emission over China in November 2016 on average had declined by 49.4 % since 2010, which is well in agreement with the bottom-up estimation of 48.0 %.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
Short summary
Short summary
Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Daisuke Goto, Yousuke Sato, Hisashi Yashiro, Kentaroh Suzuki, Eiji Oikawa, Rei Kudo, Takashi M. Nagao, and Teruyuki Nakajima
Geosci. Model Dev., 13, 3731–3768, https://doi.org/10.5194/gmd-13-3731-2020, https://doi.org/10.5194/gmd-13-3731-2020, 2020
Short summary
Short summary
We executed a global aerosol model over 3 years with the finest grid size in the world. The results elucidated that global annual averages of parameters associated with the aerosols were generally comparable to those obtained from a low-resolution model (LRM), but spatiotemporal variabilities of the aerosol components and their associated parameters provided better results closer to the observations than those from the LRM. This study clarified the advantages of the high-resolution model.
Daisuke Goto, Yu Morino, Toshimasa Ohara, Tsuyoshi Thomas Sekiyama, Junya Uchida, and Teruyuki Nakajima
Atmos. Chem. Phys., 20, 3589–3607, https://doi.org/10.5194/acp-20-3589-2020, https://doi.org/10.5194/acp-20-3589-2020, 2020
Short summary
Short summary
To obtain reliable distribution of atmospheric Cs-137 emitted from the Fukushima accident, we proposed a multi-model ensemble (MME) method using observations. We found the MME-estimated Cs-137 concentrations using all available observations had lower bias, lower uncertainty, higher correlation and higher precision against the observations compared to single-model results. It can be applied not only to the Cs-137 distribution but also any atmospheric materials such as PM2.5 distribution.
Yueming Cheng, Tie Dai, Daisuke Goto, Nick A. J. Schutgens, Guangyu Shi, and Teruyuki Nakajima
Atmos. Chem. Phys., 19, 13445–13467, https://doi.org/10.5194/acp-19-13445-2019, https://doi.org/10.5194/acp-19-13445-2019, 2019
Short summary
Short summary
Aerosol vertical information is critical to quantify the influences of aerosol on the climate and environment; however, large uncertainties still persist in model simulations. Global aerosol vertical distributions are more accurately simulated by assimilating the vertical aerosol extinction coefficients from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP).
Hiroaki Tatebe, Tomoo Ogura, Tomoko Nitta, Yoshiki Komuro, Koji Ogochi, Toshihiko Takemura, Kengo Sudo, Miho Sekiguchi, Manabu Abe, Fuyuki Saito, Minoru Chikira, Shingo Watanabe, Masato Mori, Nagio Hirota, Yoshio Kawatani, Takashi Mochizuki, Kei Yoshimura, Kumiko Takata, Ryouta O'ishi, Dai Yamazaki, Tatsuo Suzuki, Masao Kurogi, Takahito Kataoka, Masahiro Watanabe, and Masahide Kimoto
Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, https://doi.org/10.5194/gmd-12-2727-2019, 2019
Short summary
Short summary
For a deeper understanding of a wide range of climate science issues, the latest version of the Japanese climate model, called MIROC6, was developed. The climate model represents observed mean climate and climate variations well, for example tropical precipitation, the midlatitude westerlies, and the East Asian monsoon, which influence human activity all over the world. The improved climate simulations could add reliability to climate predictions under global warming.
Colin M. Zarzycki, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Kevin A. Reed, Paul A. Ullrich, David M. Hall, Mark A. Taylor, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Xi Chen, Lucas Harris, Marco Giorgetta, Daniel Reinert, Christian Kühnlein, Robert Walko, Vivian Lee, Abdessamad Qaddouri, Monique Tanguay, Hiroaki Miura, Tomoki Ohno, Ryuji Yoshida, Sang-Hun Park, Joseph B. Klemp, and William C. Skamarock
Geosci. Model Dev., 12, 879–892, https://doi.org/10.5194/gmd-12-879-2019, https://doi.org/10.5194/gmd-12-879-2019, 2019
Short summary
Short summary
We summarize the results of the Dynamical Core Model Intercomparison Project's idealized supercell test case. Supercells are storm-scale weather phenomena that are a key target for next-generation, non-hydrostatic weather prediction models. We show that the dynamical cores of most global numerical models converge between approximately 1 and 0.5 km grid spacing for this test, although differences in final solution exist, particularly due to differing grid discretizations and numerical diffusion.
Daniel T. McCoy, Paul R. Field, Gregory S. Elsaesser, Alejandro Bodas-Salcedo, Brian H. Kahn, Mark D. Zelinka, Chihiro Kodama, Thorsten Mauritsen, Benoit Vanniere, Malcolm Roberts, Pier L. Vidale, David Saint-Martin, Aurore Voldoire, Rein Haarsma, Adrian Hill, Ben Shipway, and Jonathan Wilkinson
Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, https://doi.org/10.5194/acp-19-1147-2019, 2019
Short summary
Short summary
The largest single source of uncertainty in the climate sensitivity predicted by global climate models is how much low-altitude clouds change as the climate warms. Models predict that the amount of liquid within and the brightness of low-altitude clouds increase in the extratropics with warming. We show that increased fluxes of moisture into extratropical storms in the midlatitudes explain the majority of the observed trend and the modeled increase in liquid water within these storms.
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018, https://doi.org/10.5194/gmd-11-5027-2018, 2018
Short summary
Short summary
This paper provides an overview of a coordinated international experiment to determine the strengths and weaknesses in how climate models treat snow. The models will be assessed at point locations using high-quality reference measurements and globally using satellite-derived datasets. How well climate models simulate snow-related processes is important because changing snow cover is an important part of the global climate system and provides an important freshwater resource for human use.
Takashi Arakawa, Takahiro Inoue, Hisashi Yashiro, and Masaki Satoh
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-147, https://doi.org/10.5194/gmd-2018-147, 2018
Preprint withdrawn
Short summary
Short summary
In this paper, we discussed the design concept and implementation of a coupling software Jcup. The design concept can be summarized as dividing the function of the software into changing and not changing the values of the data and enabling users to manage and implement the function of changing the value. Based upon this concept, Jcup is constructed so that 1) remapping table is utilized as input information and 2) interpolation calculation codes can be freely implemented by users.
Allison A. Wing, Kevin A. Reed, Masaki Satoh, Bjorn Stevens, Sandrine Bony, and Tomoki Ohno
Geosci. Model Dev., 11, 793–813, https://doi.org/10.5194/gmd-11-793-2018, https://doi.org/10.5194/gmd-11-793-2018, 2018
Short summary
Short summary
RCEMIP, an intercomparison of multiple types of numerical models, is proposed. In RCEMIP, the climate system is modeled in an idealized manner with no spatial dependence of boundary conditions (i.e., sea surface temperature) or forcing (i.e., incoming sunlight). This set of simulations will be used to investigate how the amount of cloudiness changes with warming, how the clustering of clouds changes with warming, and how the state of the atmosphere in this idealized setup varies between models.
Paul A. Ullrich, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Kevin A. Reed, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Joseph Klemp, Sang-Hun Park, William Skamarock, Hiroaki Miura, Tomoki Ohno, Ryuji Yoshida, Robert Walko, Alex Reinecke, and Kevin Viner
Geosci. Model Dev., 10, 4477–4509, https://doi.org/10.5194/gmd-10-4477-2017, https://doi.org/10.5194/gmd-10-4477-2017, 2017
Short summary
Short summary
Atmospheric dynamical cores are a fundamental component of global atmospheric modeling systems and are responsible for capturing the dynamical behavior of the Earth's atmosphere. To better understand modern dynamical cores, this paper aims to provide a comprehensive review of 11 dynamical cores, drawn from modeling centers and groups that participated in the 2016 Dynamical Core Model Intercomparison Project (DCMIP) workshop and summer school.
Nick Schutgens, Svetlana Tsyro, Edward Gryspeerdt, Daisuke Goto, Natalie Weigum, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 17, 9761–9780, https://doi.org/10.5194/acp-17-9761-2017, https://doi.org/10.5194/acp-17-9761-2017, 2017
Short summary
Short summary
We estimate representativeness errors in observations due to mismatching spatio-temporal sampling, on timescales of hours to a year and length scales of 50 to 200 km, for a variety of observing systems (in situ or remote sensing ground sites, satellites with imagers or lidar, etc.) and develop strategies to reduce them. This study is relevant to the use of observations in constructing satellite L3 products, observational intercomparison and model evaluation.
Yosuke Niwa, Yosuke Fujii, Yousuke Sawa, Yosuke Iida, Akihiko Ito, Masaki Satoh, Ryoichi Imasu, Kazuhiro Tsuboi, Hidekazu Matsueda, and Nobuko Saigusa
Geosci. Model Dev., 10, 2201–2219, https://doi.org/10.5194/gmd-10-2201-2017, https://doi.org/10.5194/gmd-10-2201-2017, 2017
Short summary
Short summary
A new 4D-Var inversion system based on the icosahedral grid model, NICAM, is introduced and tested. Adding to the offline forward and adjoint models, this study has introduced the optimization method of POpULar; it does not require difficult decomposition of a matrix that establishes the correlation among the prior flux errors. In identical twin experiments of atmospheric CO2 inversion, the system successfully reproduces the spatiotemporal variations of the surface fluxes.
Masuo Nakano, Akiyoshi Wada, Masahiro Sawada, Hiromasa Yoshimura, Ryo Onishi, Shintaro Kawahara, Wataru Sasaki, Tomoe Nasuno, Munehiko Yamaguchi, Takeshi Iriguchi, Masato Sugi, and Yoshiaki Takeuchi
Geosci. Model Dev., 10, 1363–1381, https://doi.org/10.5194/gmd-10-1363-2017, https://doi.org/10.5194/gmd-10-1363-2017, 2017
Short summary
Short summary
Three 7 km mesh next-generation global models and a 20 km mesh conventional global model were run to improve tropical cyclone (TC) prediction. The 7 km mesh models reduce systematic errors in the TC track, intensity and wind radii predictions. However, the simulated TC structures and their intensities in each case are very different for each model. These results suggest that the development of more sophisticated initialization techniques and model physics is needed to further improvement.
Yosuke Niwa, Hirofumi Tomita, Masaki Satoh, Ryoichi Imasu, Yousuke Sawa, Kazuhiro Tsuboi, Hidekazu Matsueda, Toshinobu Machida, Motoki Sasakawa, Boris Belan, and Nobuko Saigusa
Geosci. Model Dev., 10, 1157–1174, https://doi.org/10.5194/gmd-10-1157-2017, https://doi.org/10.5194/gmd-10-1157-2017, 2017
Short summary
Short summary
We have developed forward and adjoint models based on NICAM-TM, as part of the 4D-Var system for atmospheric GHGs inversions. The models are computationally efficient enough to make the 4D-Var iterative calculation feasible. Trajectory analysis for high-CO2 concentration events are performed to test adjoint sensitivities; we also demonstrate the potential usefulness of our adjoint model for diagnosing tracer transport.
Kunihiko Kodera, Nawo Eguchi, Hitoshi Mukougawa, Tomoe Nasuno, and Toshihiko Hirooka
Atmos. Chem. Phys., 17, 615–625, https://doi.org/10.5194/acp-17-615-2017, https://doi.org/10.5194/acp-17-615-2017, 2017
Short summary
Short summary
An exceptional strengthening of the middle atmospheric subtropical jet occurred without an apparent relationship with the tropospheric circulation. The analysis of this event demonstrated downward penetration of stratospheric influence to the troposphere: in the north polar region amplification of planetary wave occurred due to a deflection by the strong middle atmospheric subtropical jet, whereas in the tropics, increased tropopause temperature suppressed equatorial convective activity.
Reindert J. Haarsma, Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, Susanna Corti, Neven S. Fučkar, Virginie Guemas, Jost von Hardenberg, Wilco Hazeleger, Chihiro Kodama, Torben Koenigk, L. Ruby Leung, Jian Lu, Jing-Jia Luo, Jiafu Mao, Matthew S. Mizielinski, Ryo Mizuta, Paulo Nobre, Masaki Satoh, Enrico Scoccimarro, Tido Semmler, Justin Small, and Jin-Song von Storch
Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, https://doi.org/10.5194/gmd-9-4185-2016, 2016
Short summary
Short summary
Recent progress in computing power has enabled climate models to simulate more processes in detail and on a smaller scale. Here we present a common protocol for these high-resolution runs that will foster the analysis and understanding of the impact of model resolution on the simulated climate. These runs will also serve as a more reliable source for assessing climate risks that are associated with small-scale weather phenomena such as tropical cyclones.
Hisashi Yashiro, Koji Terasaki, Takemasa Miyoshi, and Hirofumi Tomita
Geosci. Model Dev., 9, 2293–2300, https://doi.org/10.5194/gmd-9-2293-2016, https://doi.org/10.5194/gmd-9-2293-2016, 2016
Short summary
Short summary
We propose the design and implementation of an ensemble data assimilation framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file I/O and multi-node communication. With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework promises drastic reduction of total execution time.
Nick A. J. Schutgens, Edward Gryspeerdt, Natalie Weigum, Svetlana Tsyro, Daisuke Goto, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 16, 6335–6353, https://doi.org/10.5194/acp-16-6335-2016, https://doi.org/10.5194/acp-16-6335-2016, 2016
Short summary
Short summary
We show that evaluating global aerosol model data with observations of very different spatial scales (200 vs. 10 km) can lead to large discrepancies, solely due to different spatial sampling. Strategies for reducing these sampling errors are developed and tested using a set of high-resolution model simulations.
S. Nishizawa, H. Yashiro, Y. Sato, Y. Miyamoto, and H. Tomita
Geosci. Model Dev., 8, 3393–3419, https://doi.org/10.5194/gmd-8-3393-2015, https://doi.org/10.5194/gmd-8-3393-2015, 2015
Short summary
Short summary
The influence of the large grid aspect ratio of horizontal to vertical grid spacing traditionally used in meteorological large-eddy simulations on simulated turbulence is investigated with a series of sensitivity tests with various grid configurations. We confirmed that the grid aspect ratio should be taken into account in the sub-grid scale model to reproduce the theoretical energy spectrum. We also found that the grid aspect ratio has an influence on the turbulent statistics.
J. Leinonen, M. D. Lebsock, S. Tanelli, K. Suzuki, H. Yashiro, and Y. Miyamoto
Atmos. Meas. Tech., 8, 3493–3517, https://doi.org/10.5194/amt-8-3493-2015, https://doi.org/10.5194/amt-8-3493-2015, 2015
Short summary
Short summary
Using multiple frequencies in cloud and precipitation radars enables them to be both sensitive enough to detect thin clouds and to penetrate heavy precipitation, profiling the entire vertical structure of the atmospheric component of the water cycle. Here, we evaluate the performance of a potential future three-frequency space-based radar system by simulating its observations using data from a high-resolution global atmospheric model.
D. Goto, T. Dai, M. Satoh, H. Tomita, J. Uchida, S. Misawa, T. Inoue, H. Tsuruta, K. Ueda, C. F. S. Ng, A. Takami, N. Sugimoto, A. Shimizu, T. Ohara, and T. Nakajima
Geosci. Model Dev., 8, 235–259, https://doi.org/10.5194/gmd-8-235-2015, https://doi.org/10.5194/gmd-8-235-2015, 2015
Short summary
Short summary
An aerosol-coupled global non-hydrostatic model with a stretched-grid system has been developed to simulate aerosols on a region scale of 10 km grids. The regional simulation does require either a nesting technique or lateral boundary conditions, as opposed to general regional models. It generally reproduces monthly mean distributions of the observed sulfate and SO2 over East Asia as well as the diurnal and synoptic variations of the observed ones around the main target region, Tokyo/Japan.
N. Eguchi, K. Kodera, and T. Nasuno
Atmos. Chem. Phys., 15, 297–304, https://doi.org/10.5194/acp-15-297-2015, https://doi.org/10.5194/acp-15-297-2015, 2015
Short summary
Short summary
The dynamical coupling process between stratosphere and troposphere in the tropical tropopause layer (TTL) during stratospheric sudden warming (SSW) was investigated using simulation data of global non-hydrostatic model (NICAM) that does not use cumulus parameterization. The results suggested that increased stratospheric tropical upwelling associated with SSW induced decreased static stability in TTL, which contributes to increased convective activity and changes in its large-scale organizations
Related subject area
Atmospheric sciences
The second Met Office Unified Model–JULES Regional Atmosphere and Land configuration, RAL2
A dynamic ammonia emission model and the online coupling with WRF–Chem (WRF–SoilN–Chem v1.0): development and regional evaluation in China
SCIATRAN software package (V4.6): update and further development of aerosol, clouds, surface reflectance databases and models
Deep learning models for generation of precipitation maps based on numerical weather prediction
An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing
On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions
Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation
The impact of altering emission data precision on compression efficiency and accuracy of simulations of the community multiscale air quality model
AerSett v1.0: a simple and straightforward model for the settling speed of big spherical atmospheric aerosols
Optimization of weather forecasting for cloud cover over the European domain using the meteorological component of the Ensemble for Stochastic Integration of Atmospheric Simulations version 1.0
Bayesian transdimensional inverse reconstruction of the Fukushima Daiichi caesium 137 release
Implementation of HONO into the chemistry–climate model CHASER (V4.0): roles in tropospheric chemistry
Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)
A modern-day Mars climate in the Met Office Unified Model: dry simulations
The AirGAM 2022r1 air quality trend and prediction model
Evaluation of a cloudy cold-air pool in the Columbia River basin in different versions of the High-Resolution Rapid Refresh (HRRR) model
Comparing Sentinel-5P TROPOMI NO2 column observations with the CAMS regional air quality ensemble
Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium
Adapting a deep convolutional RNN model with imbalanced regression loss for improved spatio-temporal forecasting of extreme wind speed events in the short to medium range
ICLASS 1.1, a variational Inverse modelling framework for the Chemistry Land-surface Atmosphere Soil Slab model: description, validation, and application
ISAT v2.0: An integrated tool for nested domain configurations and model-ready emission inventories for WRF-AQM
Towards an improved representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model: RegCM
The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation
A method for transporting cloud-resolving model variance in a multiscale modeling framework
The Mission Support System (MSS v7.0.4) and its use in planning for the SouthTRAC aircraft campaign
GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): an automatic generation tool of semi-explicit mechanisms
Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework
Evaluation of high-resolution predictions of fine particulate matter and its composition in an urban area using PMCAMx-v2.0
A local data assimilation method (Local DA v1.0) and its application in a simulated typhoon case
Improved advection, resolution, performance, and community access in the new generation (version 13) of the high-performance GEOS-Chem global atmospheric chemistry model (GCHP)
Lightning assimilation in the WRF model (Version 4.1.1): technique updates and assessment of the applications from regional to hemispheric scales
Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)
Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement
Forecasting tropical cyclone tracks in the Northwest Pacific based on a deep-learning model
A local particle filter and its Gaussian mixture extension implemented with minor modifications to the LETKF
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Importance of different parameterization changes for the updated dust cycle modeling in the Community Atmosphere Model (version 6.1)
An Improved Parameterization of Sea Spray-Mediated Heat Flux Using Gaussian Quadrature: Case Studies with a Coupled CFSv2.0-WW3 System
Data fusion uncertainty-enabled methods to map street-scale hourly NO2 in Barcelona city: a case study with CALIOPE-Urban v1.0
Evaluation of the NAQFC driven by the NOAA Global Forecast System (version 16): comparison with the WRF-CMAQ during the summer 2019 FIREX-AQ campaign
A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation
Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China
A lumped species approach for the simulation of secondary organic aerosol production from intermediate-volatility organic compounds (IVOCs): application to road transport in PMCAMx-iv (v1.0)
Accelerating models for multiphase chemical kinetics through machine learning with polynomial chaos expansion and neural networks
TrackMatcher – a tool for finding intercepts in tracks of geographical positions
Recovery of sparse urban greenhouse gas emissions
A method for generating a quasi-linear convective system suitable for observing system simulation experiments
Tropospheric transport and unresolved convection: numerical experiments with CLaMS 2.0/MESSy
AMORE-Isoprene v1.0: A new reduced mechanism for gas-phase isoprene oxidation
Mike Bush, Ian Boutle, John Edwards, Anke Finnenkoetter, Charmaine Franklin, Kirsty Hanley, Aravindakshan Jayakumar, Huw Lewis, Adrian Lock, Marion Mittermaier, Saji Mohandas, Rachel North, Aurore Porson, Belinda Roux, Stuart Webster, and Mark Weeks
Geosci. Model Dev., 16, 1713–1734, https://doi.org/10.5194/gmd-16-1713-2023, https://doi.org/10.5194/gmd-16-1713-2023, 2023
Short summary
Short summary
Building on the baseline of RAL1, the RAL2 science configuration is used for regional modelling around the UM partnership and in operations at the Met Office. RAL2 has been tested in different parts of the world including Australia, India and the UK. RAL2 increases medium and low cloud amounts in the mid-latitudes compared to RAL1, leading to improved cloud forecasts and a reduced diurnal cycle of screen temperature. There is also a reduction in the frequency of heavier precipitation rates.
Chuanhua Ren, Xin Huang, Tengyu Liu, Yu Song, Zhang Wen, Xuejun Liu, Aijun Ding, and Tong Zhu
Geosci. Model Dev., 16, 1641–1659, https://doi.org/10.5194/gmd-16-1641-2023, https://doi.org/10.5194/gmd-16-1641-2023, 2023
Short summary
Short summary
Ammonia in the atmosphere has wide impacts on the ecological environment and air quality, and its emission from soil volatilization is highly sensitive to meteorology, making it challenging to be well captured in models. We developed a dynamic emission model capable of calculating ammonia emission interactively with meteorological and soil conditions. Such a coupling of soil emission with meteorology provides a better understanding of ammonia emission and its contribution to atmospheric aerosol.
Linlu Mei, Vladimir Rozanov, Alexei Rozanov, and John P. Burrows
Geosci. Model Dev., 16, 1511–1536, https://doi.org/10.5194/gmd-16-1511-2023, https://doi.org/10.5194/gmd-16-1511-2023, 2023
Short summary
Short summary
This paper summarizes recent developments of aerosol, cloud and surface reflectance databases and models in the framework of the software package SCIATRAN. These updates and developments extend the capabilities of the radiative transfer modeling, especially by accounting for different kinds of vertical inhomogeneties. Vertically inhomogeneous clouds and different aerosol types can be easily accounted for within SCIATRAN (V4.6). The widely used surface models and databases are now available.
Adrian Rojas-Campos, Michael Langguth, Martin Wittenbrink, and Gordon Pipa
Geosci. Model Dev., 16, 1467–1480, https://doi.org/10.5194/gmd-16-1467-2023, https://doi.org/10.5194/gmd-16-1467-2023, 2023
Short summary
Short summary
Our paper presents an alternative approach for generating high-resolution precipitation maps based on the nonlinear combination of the complete set of variables of the numerical weather predictions. This process combines the super-resolution task with the bias correction in a single step, generating high-resolution corrected precipitation maps with a lead time of 3 h. We used using deep learning algorithms to combine the input information and increase the accuracy of the precipitation maps.
Robin N. Thor, Mariano Mertens, Sigrun Matthes, Mattia Righi, Johannes Hendricks, Sabine Brinkop, Phoebe Graf, Volker Grewe, Patrick Jöckel, and Steven Smith
Geosci. Model Dev., 16, 1459–1466, https://doi.org/10.5194/gmd-16-1459-2023, https://doi.org/10.5194/gmd-16-1459-2023, 2023
Short summary
Short summary
We report on an inconsistency in the latitudinal distribution of aviation emissions between two versions of a data product which is widely used by researchers. From the available documentation, we do not expect such an inconsistency. We run a chemistry–climate model to compute the effect of the inconsistency in emissions on atmospheric chemistry and radiation and find that the radiative forcing associated with aviation ozone is 7.6 % higher when using the less recent version of the data.
Stefano Della Fera, Federico Fabiano, Piera Raspollini, Marco Ridolfi, Ugo Cortesi, Flavio Barbara, and Jost von Hardenberg
Geosci. Model Dev., 16, 1379–1394, https://doi.org/10.5194/gmd-16-1379-2023, https://doi.org/10.5194/gmd-16-1379-2023, 2023
Short summary
Short summary
The long-term comparison between observed and simulated outgoing longwave radiances represents a strict test to evaluate climate model performance. In this work, 9 years of synthetic spectrally resolved radiances, simulated online on the basis of the atmospheric fields predicted by the EC-Earth global climate model (v3.3.3) in clear-sky conditions, are compared to IASI spectral radiance climatology in order to detect model biases in temperature and humidity at different atmospheric levels.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
Short summary
Short summary
Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Michael S. Walters and David C. Wong
Geosci. Model Dev., 16, 1179–1190, https://doi.org/10.5194/gmd-16-1179-2023, https://doi.org/10.5194/gmd-16-1179-2023, 2023
Short summary
Short summary
A typical numerical simulation that associates with a large amount of input and output data, applying popular compression software, gzip or bzip2, on data is one good way to mitigate data storage burden. This article proposes a simple technique to alter input, output, or input and output by keeping a specific number of significant digits in data and demonstrates an enhancement in compression efficiency on the altered data but maintains similar statistical performance of the numerical simulation.
Sylvain Mailler, Laurent Menut, Arineh Cholakian, and Romain Pennel
Geosci. Model Dev., 16, 1119–1127, https://doi.org/10.5194/gmd-16-1119-2023, https://doi.org/10.5194/gmd-16-1119-2023, 2023
Short summary
Short summary
Large or even
giantparticles of mineral dust exist in the atmosphere but, so far, solving an non-linear equation was needed to calculate the speed at which they fall in the atmosphere. The model we present, AerSett v1.0 (AERosol SETTling version 1.0), provides a new and simple way of calculating their free-fall velocity in the atmosphere, which will be useful to anyone trying to understand and represent adequately the transport of giant dust particles by the wind.
Yen-Sen Lu, Garrett H. Good, and Hendrik Elbern
Geosci. Model Dev., 16, 1083–1104, https://doi.org/10.5194/gmd-16-1083-2023, https://doi.org/10.5194/gmd-16-1083-2023, 2023
Short summary
Short summary
The Weather Forecasting and Research (WRF) model consists of many parameters and options that can be adapted to different conditions. This expansive sensitivity study uses a large-scale simulation system to determine the most suitable options for predicting cloud cover in Europe for deterministic and probabilistic weather predictions for day-ahead forecasting simulations.
Joffrey Dumont Le Brazidec, Marc Bocquet, Olivier Saunier, and Yelva Roustan
Geosci. Model Dev., 16, 1039–1052, https://doi.org/10.5194/gmd-16-1039-2023, https://doi.org/10.5194/gmd-16-1039-2023, 2023
Short summary
Short summary
When radionuclides are released into the atmosphere, the assessment of the consequences depends on the evaluation of the magnitude and temporal evolution of the release, which can be highly variable as in the case of Fukushima Daiichi.
Here, we propose Bayesian inverse modelling methods and the reversible-jump Markov chain Monte Carlo technique, which allows one to evaluate the temporal variability of the release and to integrate different types of information in the source reconstruction.
Phuc Thi Minh Ha, Yugo Kanaya, Fumikazu Taketani, Maria Dolores Andrés Hernández, Benjamin Schreiner, Klaus Pfeilsticker, and Kengo Sudo
Geosci. Model Dev., 16, 927–960, https://doi.org/10.5194/gmd-16-927-2023, https://doi.org/10.5194/gmd-16-927-2023, 2023
Short summary
Short summary
HONO affects tropospheric oxidizing capacity; thus, it is implemented into the chemistry–climate model CHASER. The model substantially underpredicts daytime HONO, while nitrate photolysis on surfaces can supplement the daytime HONO budget. Current HONO chemistry predicts reductions of 20.4 % for global tropospheric NOx, 40–67 % for OH, and 30–45 % for O3 in the summer North Pacific. In contrast, OH and O3 winter levels in China are greatly enhanced.
Ryan Vella, Matthew Forrest, Jos Lelieveld, and Holger Tost
Geosci. Model Dev., 16, 885–906, https://doi.org/10.5194/gmd-16-885-2023, https://doi.org/10.5194/gmd-16-885-2023, 2023
Short summary
Short summary
Biogenic volatile organic compounds (BVOCs) are released by vegetation and have a major impact on atmospheric chemistry and aerosol formation. Non-interacting vegetation constrains the majority of numerical models used to estimate global BVOC emissions, and thus, the effects of changing vegetation on emissions are not addressed. In this work, we replace the offline vegetation with dynamic vegetation states by linking a chemistry–climate model with a global dynamic vegetation model.
Danny McCulloch, Denis E. Sergeev, Nathan Mayne, Matthew Bate, James Manners, Ian Boutle, Benjamin Drummond, and Kristzian Kohary
Geosci. Model Dev., 16, 621–657, https://doi.org/10.5194/gmd-16-621-2023, https://doi.org/10.5194/gmd-16-621-2023, 2023
Short summary
Short summary
We present results from the Met Office Unified Model (UM) to study the dry Martian climate. We describe our model set-up conditions and run two scenarios, with radiatively active/inactive dust. We compare both scenarios to results from an existing Mars climate model, the planetary climate model. We find good agreement in winds and air temperatures, but dust amounts differ between models. This study highlights the importance of using the UM for future Mars research.
Sam-Erik Walker, Sverre Solberg, Philipp Schneider, and Cristina Guerreiro
Geosci. Model Dev., 16, 573–595, https://doi.org/10.5194/gmd-16-573-2023, https://doi.org/10.5194/gmd-16-573-2023, 2023
Short summary
Short summary
We have developed a statistical model for estimating trends in the daily air quality observations of NO2, O3, PM10 and PM2.5, adjusting for trends and short-term variations in meteorology. The model is general and may also be used for prediction purposes, including forecasting. It has been applied in a recent comprehensive study in Europe. Significant declines are shown for the pollutants from 2005 to 2019, mainly due to reductions in emissions not attributable to changes in meteorology.
Bianca Adler, James M. Wilczak, Jaymes Kenyon, Laura Bianco, Irina V. Djalalova, Joseph B. Olson, and David D. Turner
Geosci. Model Dev., 16, 597–619, https://doi.org/10.5194/gmd-16-597-2023, https://doi.org/10.5194/gmd-16-597-2023, 2023
Short summary
Short summary
Rapid changes in wind speed make the integration of wind energy produced during persistent orographic cold-air pools difficult to integrate into the electrical grid. By evaluating three versions of NOAA’s High-Resolution Rapid Refresh model, we demonstrate how model developments targeted during the second Wind Forecast Improvement Project improve the forecast of a persistent cold-air pool event.
John Douros, Henk Eskes, Jos van Geffen, K. Folkert Boersma, Steven Compernolle, Gaia Pinardi, Anne-Marlene Blechschmidt, Vincent-Henri Peuch, Augustin Colette, and Pepijn Veefkind
Geosci. Model Dev., 16, 509–534, https://doi.org/10.5194/gmd-16-509-2023, https://doi.org/10.5194/gmd-16-509-2023, 2023
Short summary
Short summary
We focus on the challenges associated with comparing atmospheric composition models with satellite products such as tropospheric NO2 columns. The aim is to highlight the methodological difficulties and propose sound ways of doing such comparisons. Building on the comparisons, a new satellite product is proposed and made available, which takes advantage of higher-resolution, regional atmospheric modelling to improve estimates of troposheric NO2 columns over Europe.
Catalina Poraicu, Jean-François Müller, Trissevgeni Stavrakou, Dominique Fonteyn, Frederik Tack, Felix Deutsch, Quentin Laffineur, Roeland Van Malderen, and Nele Veldeman
Geosci. Model Dev., 16, 479–508, https://doi.org/10.5194/gmd-16-479-2023, https://doi.org/10.5194/gmd-16-479-2023, 2023
Short summary
Short summary
High-resolution WRF-Chem simulations are conducted over Antwerp, Belgium, in June 2019 and evaluated using meteorological data and in situ, airborne, and spaceborne NO2 measurements. An intercomparison of model, aircraft, and TROPOMI NO2 columns is conducted to characterize biases in versions 1.3.1 and 2.3.1 of the satellite product. A mass balance method is implemented to provide improved emissions for simulating NO2 distribution over the study area.
Daan R. Scheepens, Irene Schicker, Kateřina Hlaváčková-Schindler, and Claudia Plant
Geosci. Model Dev., 16, 251–270, https://doi.org/10.5194/gmd-16-251-2023, https://doi.org/10.5194/gmd-16-251-2023, 2023
Short summary
Short summary
The production of wind energy is increasing rapidly and relies heavily on atmospheric conditions. To ensure power grid stability, accurate predictions of wind speed are needed, especially in the short range and for extreme wind speed ranges. In this work, we demonstrate the forecasting skills of a data-driven deep learning model with model adaptations to suit higher wind speed ranges. The resulting model can be applied to other data and parameters, too, to improve nowcasting predictions.
Peter J. M. Bosman and Maarten C. Krol
Geosci. Model Dev., 16, 47–74, https://doi.org/10.5194/gmd-16-47-2023, https://doi.org/10.5194/gmd-16-47-2023, 2023
Short summary
Short summary
We describe an inverse modelling framework constructed around a simple model for the atmospheric boundary layer. This framework can be fed with various observation types to study the boundary layer and land–atmosphere exchange. With this framework, it is possible to estimate model parameters and the associated uncertainties. Some of these parameters are difficult to obtain directly by observations. An example application for a grassland in the Netherlands is included.
Kun Wang, Chao Gao, Haofan Wang, Kai Wu, Qingqing Tong, Mo Dan, Kaiyun Liu, and Xiaohui Ji
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-266, https://doi.org/10.5194/gmd-2022-266, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This study established an easy-to-use and integrated framework on model ready emission inventory for Weather Research and Forecasting (WRF)-Air quality numerical model (AQM). A free tool named ISAT (Inventory Spatial Allocation Tool) was developed based on this framework. ISAT help user complete the workflow from WRF nested domain configuration to model ready emission inventory for AQM with regional emission inventory and shapefile for target region.
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, https://doi.org/10.5194/gmd-16-1-2023, 2023
Short summary
Short summary
Accurate representation of aerosols in climate models is critical for minimizing the uncertainty in climate projections. Here, we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model (RegCM4) and show that it improves model performance significantly against in situ, reanalysis, and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Chengzhu Zhang, Jean-Christophe Golaz, Ryan Forsyth, Tom Vo, Shaocheng Xie, Zeshawn Shaheen, Gerald L. Potter, Xylar S. Asay-Davis, Charles S. Zender, Wuyin Lin, Chih-Chieh Chen, Chris R. Terai, Salil Mahajan, Tian Zhou, Karthik Balaguru, Qi Tang, Cheng Tao, Yuying Zhang, Todd Emmenegger, Susannah Burrows, and Paul A. Ullrich
Geosci. Model Dev., 15, 9031–9056, https://doi.org/10.5194/gmd-15-9031-2022, https://doi.org/10.5194/gmd-15-9031-2022, 2022
Short summary
Short summary
Earth system model (ESM) developers run automated analysis tools on data from candidate models to inform model development. This paper introduces a new Python package, E3SM Diags, that has been developed to support ESM development and use routinely in the development of DOE's Energy Exascale Earth System Model. This tool covers a set of essential diagnostics to evaluate the mean physical climate from simulations, as well as several process-oriented and phenomenon-based evaluation diagnostics.
Walter Hannah and Kyle Pressel
Geosci. Model Dev., 15, 8999–9013, https://doi.org/10.5194/gmd-15-8999-2022, https://doi.org/10.5194/gmd-15-8999-2022, 2022
Short summary
Short summary
A multiscale modeling framework couples two models of the atmosphere that each cover different scale ranges. Traditionally, fluctuations in the small-scale model are not transported by the flow on the large-scale model grid, but this is hypothesized to be responsible for a persistent, unphysical checkerboard pattern. A method is presented to facilitate the transport of these small-scale fluctuations, analogous to how small-scale clouds and turbulence are transported in the real atmosphere.
Reimar Bauer, Jens-Uwe Grooß, Jörn Ungermann, May Bär, Markus Geldenhuys, and Lars Hoffmann
Geosci. Model Dev., 15, 8983–8997, https://doi.org/10.5194/gmd-15-8983-2022, https://doi.org/10.5194/gmd-15-8983-2022, 2022
Short summary
Short summary
The Mission Support System (MSS) is an open source software package that has been used for planning flight tracks of scientific aircraft in multiple measurement campaigns during the last decade. Here, we describe the MSS software and its use during the SouthTRAC measurement campaign in 2019. As an example for how the MSS software is used in conjunction with many datasets, we describe the planning of a single flight probing orographic gravity waves propagating up into the lower mesosphere.
Zhizhao Wang, Florian Couvidat, and Karine Sartelet
Geosci. Model Dev., 15, 8957–8982, https://doi.org/10.5194/gmd-15-8957-2022, https://doi.org/10.5194/gmd-15-8957-2022, 2022
Short summary
Short summary
Air quality models need to reliably predict secondary organic aerosols (SOAs) at a reasonable computational cost. Thus, we developed GENOA v1.0, a mechanism reduction algorithm that preserves the accuracy of detailed gas-phase chemical mechanisms for SOA formation, thereby improving the practical use of actual chemistry in SOA models. With GENOA, a near-explicit chemical scheme was reduced to 2 % of its original size and computational time, with an average error of less than 3 %.
Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz
Geosci. Model Dev., 15, 8913–8930, https://doi.org/10.5194/gmd-15-8913-2022, https://doi.org/10.5194/gmd-15-8913-2022, 2022
Short summary
Short summary
We examine the effects of spatially aggregated upstream information as input for a deep learning model forecasting near-surface ozone levels. Using aggregated data from one upstream sector (45°) improves the forecast by ~ 10 % for 4 prediction days. Three upstream sectors improve the forecasts by ~ 14 % on the first 2 d only. Our results serve as an orientation for other researchers or environmental agencies focusing on pointwise time-series predictions, for example, due to regulatory purposes.
Brian T. Dinkelacker, Pablo Garcia Rivera, Ioannis Kioutsioukis, Peter J. Adams, and Spyros N. Pandis
Geosci. Model Dev., 15, 8899–8912, https://doi.org/10.5194/gmd-15-8899-2022, https://doi.org/10.5194/gmd-15-8899-2022, 2022
Short summary
Short summary
The performance of a chemical transport model in reproducing PM2.5 concentrations and composition was evaluated at the finest scale using measurements from regulatory sites as well as a network of low-cost monitors. Total PM2.5 mass is reproduced well by the model during the winter when compared to regulatory measurements, but in the summer PM2.5 is underpredicted, mainly due to difficulties in reproducing regional secondary organic aerosol levels.
Shizhang Wang and Xiaoshi Qiao
Geosci. Model Dev., 15, 8869–8897, https://doi.org/10.5194/gmd-15-8869-2022, https://doi.org/10.5194/gmd-15-8869-2022, 2022
Short summary
Short summary
A local data assimilation scheme (Local DA v1.0) was proposed to leverage the advantage of hybrid covariance, multiscale localization, and parallel computation. The Local DA can perform covariance localization in model space, observation space, or both spaces. The Local DA that used the hybrid covariance and double-space localization produced the lowest analysis and forecast errors among all observing system simulation experiments.
Randall V. Martin, Sebastian D. Eastham, Liam Bindle, Elizabeth W. Lundgren, Thomas L. Clune, Christoph A. Keller, William Downs, Dandan Zhang, Robert A. Lucchesi, Melissa P. Sulprizio, Robert M. Yantosca, Yanshun Li, Lucas Estrada, William M. Putman, Benjamin M. Auer, Atanas L. Trayanov, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 15, 8731–8748, https://doi.org/10.5194/gmd-15-8731-2022, https://doi.org/10.5194/gmd-15-8731-2022, 2022
Short summary
Short summary
Atmospheric chemistry models must be able to operate both online as components of Earth system models and offline as standalone models. The widely used GEOS-Chem model operates both online and offline, but the classic offline version is not suitable for massively parallel simulations. We describe a new generation of the offline high-performance GEOS-Chem (GCHP) that enables high-resolution simulations on thousands of cores, including on the cloud, with improved access, performance, and accuracy.
Daiwen Kang, Nicholas K. Heath, Robert C. Gilliam, Tanya L. Spero, and Jonathan E. Pleim
Geosci. Model Dev., 15, 8561–8579, https://doi.org/10.5194/gmd-15-8561-2022, https://doi.org/10.5194/gmd-15-8561-2022, 2022
Short summary
Short summary
A lightning assimilation (LTA) technique implemented in the WRF model's Kain–Fritsch (KF) convective scheme is updated and applied to simulations from regional to hemispheric scales using observed lightning flashes from ground-based lightning detection networks. Different user-toggled options associated with the KF scheme on simulations with and without LTA are assessed. The model's performance is improved significantly by LTA, but it is sensitive to various factors.
Sujeong Lim, Hyeon-Ju Gim, Ebony Lee, Seungyeon Lee, Won Young Lee, Yong Hee Lee, Claudio Cassardo, and Seon Ki Park
Geosci. Model Dev., 15, 8541–8559, https://doi.org/10.5194/gmd-15-8541-2022, https://doi.org/10.5194/gmd-15-8541-2022, 2022
Short summary
Short summary
The land surface model (LSM) contains various uncertain parameters, which are obtained by the empirical relations reflecting the specific local region and can be a source of uncertainty. To seek the optimal parameter values in the snow-related processes of the Noah LSM over South Korea, we have implemented an optimization algorithm, a micro-genetic algorithm using the observations. As a result, the optimized snow parameters improve snowfall prediction.
Haochen Sun, Jimmy C. H. Fung, Yiang Chen, Zhenning Li, Dehao Yuan, Wanying Chen, and Xingcheng Lu
Geosci. Model Dev., 15, 8439–8452, https://doi.org/10.5194/gmd-15-8439-2022, https://doi.org/10.5194/gmd-15-8439-2022, 2022
Short summary
Short summary
This study developed a novel deep-learning layer, the broadcasting layer, to build an end-to-end LSTM-based deep-learning model for regional air pollution forecast. By combining the ground observation, WRF-CMAQ simulation, and the broadcasting LSTM deep-learning model, forecast accuracy has been significantly improved when compared to other methods. The broadcasting layer and its variants can also be applied in other research areas to supersede the traditional numerical interpolation methods.
Liang Wang, Bingcheng Wan, Shaohui Zhou, Haofei Sun, and Zhiqiu Gao
EGUsphere, https://doi.org/10.5194/egusphere-2022-1216, https://doi.org/10.5194/egusphere-2022-1216, 2022
Short summary
Short summary
The past 24-h TC trajectory and meteorological field data were used to forecast TC tracks in the Northwest Pacific from hours 6–72 based on GRU_CNN we proposed in this paper, which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
Shunji Kotsuki, Takemasa Miyoshi, Keiichi Kondo, and Roland Potthast
Geosci. Model Dev., 15, 8325–8348, https://doi.org/10.5194/gmd-15-8325-2022, https://doi.org/10.5194/gmd-15-8325-2022, 2022
Short summary
Short summary
Data assimilation plays an important part in numerical weather prediction (NWP) in terms of combining forecasted states and observations. While data assimilation methods in NWP usually assume the Gaussian error distribution, some variables in the atmosphere, such as precipitation, are known to have non-Gaussian error statistics. This study extended a widely used ensemble data assimilation algorithm to enable the assimilation of more non-Gaussian observations.
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
Geosci. Model Dev., 15, 8295–8323, https://doi.org/10.5194/gmd-15-8295-2022, https://doi.org/10.5194/gmd-15-8295-2022, 2022
Short summary
Short summary
In light of recent global warming, we aim to improve methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.
Longlei Li, Natalie M. Mahowald, Jasper F. Kok, Xiaohong Liu, Mingxuan Wu, Danny M. Leung, Douglas S. Hamilton, Louisa K. Emmons, Yue Huang, Neil Sexton, Jun Meng, and Jessica Wan
Geosci. Model Dev., 15, 8181–8219, https://doi.org/10.5194/gmd-15-8181-2022, https://doi.org/10.5194/gmd-15-8181-2022, 2022
Short summary
Short summary
This study advances mineral dust parameterizations in the Community Atmospheric Model (CAM; version 6.1). Efforts include 1) incorporating a more physically based dust emission scheme; 2) updating the dry deposition scheme; and 3) revising the gravitational settling velocity to account for dust asphericity. Substantial improvements achieved with these updates can help accurately quantify dust–climate interactions using CAM, such as the dust-radiation and dust–cloud interactions.
Ruizi Shi and Fanghua Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-233, https://doi.org/10.5194/gmd-2022-233, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
Based on Gaussian Quadrature method, a fast parameterization scheme of sea spray-mediated heat flux is developed. Compared with the widely-used single-radius scheme, the new scheme shows a better agreement with the full spectrum integral of spray-flux. The new scheme is evaluated in a coupled modeling system, and the simulations of sea surface temperature, wind speed and wave height are improved. Thereby, the new scheme has a great potential to be used in coupled modeling systems.
Alvaro Criado, Jan Mateu Armengol, Hervé Petetin, Daniel Rodríguez-Rey, Jaime Benavides, Marc Guevara, Carlos Pérez García-Pando, Albert Soret, and Oriol Jorba
EGUsphere, https://doi.org/10.5194/egusphere-2022-1147, https://doi.org/10.5194/egusphere-2022-1147, 2022
Short summary
Short summary
The goal of this work is to derive and evaluate a general statistical post-processing tool specifically designed for the street scale that can be applied to any urban air quality system. Our data-fusion methodology corrects NO2 fields based on continuous hourly observations and experimental campaigns. This study able us to obtain exceedance probability maps of air quality standards. In 2019, 13 % of the Barcelona area had a 70 % or higher probability of exceeding the annual legal NO2 limit.
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022, https://doi.org/10.5194/gmd-15-7977-2022, 2022
Short summary
Short summary
This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
Elena Fillola, Raul Santos-Rodriguez, Alistair Manning, Simon O'Doherty, and Matt Rigby
EGUsphere, https://doi.org/10.5194/egusphere-2022-1174, https://doi.org/10.5194/egusphere-2022-1174, 2022
Short summary
Short summary
Lagrangian particle dispersion models are used extensively for the estimation of greenhouse gas (GHG) fluxes using atmospheric observations. However, these models do not scale well as data volumes increase. Here, we develop a proof-of-concept machine learning emulator that can produce outputs similar to those of the dispersion model, but 50,000 times faster, using only meteorological inputs. This works demonstrates the potential of machine learning to accelerate GHG estimations across the globe.
Zhiquan Liu, Chris Snyder, Jonathan J. Guerrette, Byoung-Joo Jung, Junmei Ban, Steven Vahl, Yali Wu, Yannick Trémolet, Thomas Auligné, Benjamin Ménétrier, Anna Shlyaeva, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev., 15, 7859–7878, https://doi.org/10.5194/gmd-15-7859-2022, https://doi.org/10.5194/gmd-15-7859-2022, 2022
Short summary
Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
Li Fang, Jianbing Jin, Arjo Segers, Hai Xiang Lin, Mijie Pang, Cong Xiao, Tuo Deng, and Hong Liao
Geosci. Model Dev., 15, 7791–7807, https://doi.org/10.5194/gmd-15-7791-2022, https://doi.org/10.5194/gmd-15-7791-2022, 2022
Short summary
Short summary
This study proposes a regional feature selection-based machine learning system to predict short-term air quality in China. The system has a tool that can figure out the importance of input data for better prediction. It provides large-scale air quality prediction that exhibits improved interpretability, fewer training costs, and higher accuracy compared with a standard machine learning system. It can act as an early warning for citizens and reduce exposure to PM2.5 and other air pollutants.
Stella E. I. Manavi and Spyros N. Pandis
Geosci. Model Dev., 15, 7731–7749, https://doi.org/10.5194/gmd-15-7731-2022, https://doi.org/10.5194/gmd-15-7731-2022, 2022
Short summary
Short summary
The paper describes the first step towards the development of a simulation framework for the chemistry and secondary organic aerosol production of intermediate-volatility organic compounds (IVOCs). These compounds can be a significant source of organic particulate matter. Our approach treats IVOCs as lumped compounds that retain their chemical characteristics. Estimated IVOC emissions from road transport were a factor of 8 higher than emissions used in previous applications.
Thomas Berkemeier, Matteo Krüger, Aryeh Feinberg, Marcel Müller, Ulrich Pöschl, and Ulrich K. Krieger
EGUsphere, https://doi.org/10.5194/egusphere-2022-1093, https://doi.org/10.5194/egusphere-2022-1093, 2022
Short summary
Short summary
Kinetic multi-layer models (KM) successfully describe heterogeneous and multiphase atmospheric chemistry. In applications requiring repeated execution, however, these models can be too expensive. We trained machine learning surrogate models on output of the model KM-SUB and achieve high correlations. The surrogate models run orders of magnitudes faster, which suggests potential applicability in global optimization tasks and as sub-modules in large-scale atmospheric models.
Peter Bräuer and Matthias Tesche
Geosci. Model Dev., 15, 7557–7572, https://doi.org/10.5194/gmd-15-7557-2022, https://doi.org/10.5194/gmd-15-7557-2022, 2022
Short summary
Short summary
This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting of data in the vicinity of intersections to achieve the optimal combination of various data sets.
Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich
Geosci. Model Dev., 15, 7533–7556, https://doi.org/10.5194/gmd-15-7533-2022, https://doi.org/10.5194/gmd-15-7533-2022, 2022
Short summary
Short summary
Gaussian priors (GPs) used in least squares inversion do not reflect the true distributions of greenhouse gas emissions well. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that the application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.
Jonathan D. Labriola and Louis J. Wicker
EGUsphere, https://doi.org/10.5194/egusphere-2022-1033, https://doi.org/10.5194/egusphere-2022-1033, 2022
Short summary
Short summary
Observing system simulation experiments (OSSEs) are simulated case studies used to understand how different assimilated weather observations impact forecast skill. This study introduces the methods used to create an OSSE for a tornadic quasi-linear convective system event. These steps provide an opportunity to simulate a realistic high-impact weather event and can be used to encourage a more diverse set of OSSEs.
Paul Konopka, Mengchu Tao, Marc von Hobe, Lars Hoffmann, Corinna Kloss, Fabrizio Ravegnani, C. Michael Volk, Valentin Lauther, Andreas Zahn, Peter Hoor, and Felix Ploeger
Geosci. Model Dev., 15, 7471–7487, https://doi.org/10.5194/gmd-15-7471-2022, https://doi.org/10.5194/gmd-15-7471-2022, 2022
Short summary
Short summary
Pure trajectory-based transport models driven by meteorology derived from reanalysis products (ERA5) take into account only the resolved, advective part of transport. That means neither mixing processes nor unresolved subgrid-scale advective processes like convection are included. The Chemical Lagrangian Model of the Stratosphere (CLaMS) includes these processes. We show that isentropic mixing dominates unresolved transport. The second most important transport process is unresolved convection.
Forwood Wiser, Bryan Place, Siddhartha Sen, Havala O. T. Pye, Benjamin Yang, Daniel M. Westervelt, Daven K. Henze, Arlene M. Fiore, and V. Faye McNeill
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-240, https://doi.org/10.5194/gmd-2022-240, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
We developed an automated method, AMORE, to simplify complex chemical mechanisms. We applied AMORE to the oxidation mechanism for isoprene, an abundant biogenic volatile organic compound. Using AMORE with minimal manual adjustments to the output, we created the AMORE-isoprene mechanism, with improved accuracy and similar size to other reduced isoprene mechanisms. AMORE-Isoprene improved the accuracy of EPA’s CMAQ model compared to observations.
Cited articles
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak,
J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind,
J., Arkin, P., and Nelkin, E.: The version-2 global precipitation climatology
project (GPCP) monthly precipitation analysis (1979–present), J.
Hydrometeorol., 4, 1147–1167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2, 2003.
Aoki, T., Kuchiki, K., Niwano, M., Kodama, Y., Hosaka, M., and Tanaka, T.:
Physically based snow albedo model for calculating broadband albedos and the
solar heating profile in snowpack for general circulation models, J.
Geophys. Res., 116, D11114, https://doi.org/10.1029/2010JD015507, 2011.
Armstrong, R. L. and and Brun, E. (Eds.): Snow and climate: Physical
processes, surface energy exchange and modeling, Cambridge Univ. Press,
Cambridge, UK, 2008.
Austin, R. T. and Stephens, G. L.: Retrieval of stratus cloud microphysical
parameters using millimeter-wave radar and visible optical depth in
preparation for CloudSat: 1. Algorithm formulation, J. Geophys. Res.-Atmos.,
106, 28233–28242, https://doi.org/10.1029/2000JD000293, 2001.
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud
microphysical parameters using the CloudSat millimeter-wave radar and
temperature, J. Geophys. Res., 114, D00A23, https://doi.org/10.1029/2008JD010049, 2009.
Bodas-Salcedo, A., Webb, M. J., Brooks, M. E., Ringer, M. A., Williams, K.
D., Milton, S. F., and Wilson, D. R.: Evaluating cloud systems in the Met
Office global forecast model using simulated CloudSat radar reflectivities,
J. Geophys. Res., 113, D00A13, https://doi.org/10.1029/2007JD009620, 2008.
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L.,
Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John,
V. O.: COSP: Satellite simulation software for model assessment, B. Am.
Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011.
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus,
R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H.,
Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity,
Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015.
Chen, Y.-W., Seiki, T., Kodama, C., Satoh, M., Noda, A. T., and Yamada, Y.:
High Cloud Responses to Global Warming Simulated by Two Different Cloud
Microphysics Schemes Implemented in the Nonhydrostatic Icosahedral
Atmospheric Model (NICAM), J. Climate, 29, 5949–5964,
https://doi.org/10.1175/JCLI-D-15-0668.1, 2016.
Chen, Y.-W., Seiki, T., Kodama, C., Satoh, M., and Noda, A. T.: Impact of
precipitating ice hydrometeors on longwave radiative effect estimated by a
global cloud-system resolving model, J. Adv. Model. Earth Sy., 10,
284–296, https://doi.org/10.1002/2017MS001180, 2018.
Chepfer, H., Bony, S., Winker, D., Chiriaco, M., Dufresne, J.-L., and
Sèze, G.: Use of CALIPSO lidar observations to evaluate the cloudiness
simulated by a climate model, Geophys. Res. Lett., 35, L15704,
https://doi.org/10.1029/2008GL034207, 2008.
ECMWF: ECMWF ERA-20C, Daily, available at: https://apps.ecmwf.int/datasets/data/era20c-daily/,
last access: 21 January 2021.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J.
B.: Bulk parameterization of air–sea fluxes: updates and verification for
the COARE algorithm, J. Climate, 16, 571–591,
https://doi.org/10.1175/1520-0442(2003)016<0571:BPOASF>2.0.CO;2,
2003.
Fiedler, S., Stevens, B., and Mauritsen, T.: On the sensitivity of anthropogenic aerosol forcing to model‐internal variability and parameterizing a T womey effect, J. Adv. Model. Earth Sy., 9, 1325–1341, https://doi.org/10.1002/2017MS000932, 2017.
Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., and van Vuuren, D.: First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect, Geosci. Model Dev., 12, 989–1007, https://doi.org/10.5194/gmd-12-989-2019, 2019.
Field, P. R., Hogan, R. J., Brown, P. R. A., Illingworth, A. J., Choularton,
T. W., and Cotton, R. J.: Parametrization of ice-particle size distributions
for mid-latitude stratiform cloud, Q. J. Roy. Meteor. Soc., 131,
1997–2017, https://doi.org/10.1256/qj.04.134, 2005.
Fu, Q.: An accurate parameterization of the solar radiative properties of
cirrus clouds for climate models, J. Climate, 9, 2058–2082,
https://doi.org/10.1175/1520-0442(1996)009<2058:AAPOTS>2.0.CO;2,
1996.
Fu, Q., Yang, P., and Sun, W. B.: An accurate parameterization of the
infrared radiative properties of cirrus clouds for climate models, J. Climate,
11, 2223–2237, https://doi.org/10.1175/1520-0442(1998)011<2223:AAPOTI>2.0.CO;2, 1998.
Fukutomi, Y., Kodama, C., Yamada, Y., Noda, A. T., and Satoh, M.: Tropical
synoptic-scale wave disturbances over the western Pacific simulated by a
global cloud-system resolving model, Theor. Appl. Climatol., 124,
737–755, https://doi.org/10.1007/s00704-015-1456-4, 2016.
Gilmore, M. S., Straka, J. M., and Rasmussen, E. N.: Precipitation
uncertainty due to variations in precipitation particle parameters within a
simple microphysics scheme, Mon. Weather Rev., 132, 2610–2627,
https://doi.org/10.1175/MWR2810.1, 2004.
Goto, D., Takemura, T., and Nakajima, T.: Importance of global aerosol
modeling including secondary organic aerosol formed from monoterpene, J.
Geophys. Res., 113, D07205, https://doi.org/10.1029/2007JD009019, 2008.
Goto, D., Nakajima, T., Takemura, T., and Sudo, K.: A study of uncertainties in the sulfate distribution and its radiative forcing associated with sulfur chemistry in a global aerosol model, Atmos. Chem. Phys., 11, 10889–10910, https://doi.org/10.5194/acp-11-10889-2011, 2011.
Goto, D., Nakajima, T., Tie, D., Yashiro, H., Sato, Y., Suzuki, K., Uchida,
J., Misawa, S., Yonemoto, R., Trieu, T. T. N., Tomita, H., and Satoh, M.:
Multi-scale simulations of atmospheric pollutants using a non-hydrostatic
icosahedral atmospheric model, in: Land-Atmospheric Research Applications in
South and Southeast Asia, edited by: Vadrevu, K., Ohara, T., and Justice, C.,
Springer International Publishing, 277–302, 2018.
Goto, D., Sato, Y., Yashiro, H., Suzuki, K., Oikawa, E., Kudo, R., Nagao, T. M., and Nakajima, T.: Global aerosol simulations using NICAM.16 on a 14 km grid spacing for a climate study: improved and remaining issues relative to a lower-resolution model, Geosci. Model Dev., 13, 3731–3768, https://doi.org/10.5194/gmd-13-3731-2020, 2020.
Grabowski, W. W.: Impact of explicit atmosphere–ocean coupling on MJO-like
coherent structures in idealized aquaplanet simulations, J. Atmos. Sci.,
63, 2289–2306, https://doi.org/10.1175/JAS3740.1, 2006.
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.
Hashino, T., Satoh, M., Hagihara, Y., Kubota, T., Matsui, T., Nasuno, T., and
Okamoto, H.: Evaluating cloud microphysics from NICAM against CloudSat and
CALIPSO, J. Geophys. Res.-Atmos., 118, 7273–7292,
https://doi.org/10.1002/jgrd.50564, 2013.
Hashino, T., Satoh, M., Hagihara, Y., Kato, S., Kubota, T., Matsui, T.,
Nasuno, T., Okamoto, H., and Sekiguchi, M.: Evaluating Arctic cloud radiative
effects simulated by NICAM with A-train, J. Geophys. Res.-Atmos., 121,
7041–7063, https://doi.org/10.1002/2016JD024775, 2016.
Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A., and Stephens, G.
L.: A multipurpose radar simulation package: QuickBeam, B. Am. Meteorol.
Soc., 88, 1723–1728, https://doi.org/10.1175/BAMS-88-11-1723, 2007.
Hegglin, M., Kinnison, D., Lamarque, J.-F., and Plummer, D.: CCMI ozone in
support of CMIP6 – version 1.0. Version 20160711, Earth System Grid Federation, https://doi.org/10.22033/ESGF/input4MIPs.1115, 2016.
Hegglin, M., Kinnison, D., Lamarque, J.-F., and Plummer, D.:
input4MIPs.CMIP6.ScenarioMIP.UReading.UReading-CCMI-ssp585-1-0, Version
20181101, Earth System Grid Federation, https://doi.org/10.22033/ESGF/input4MIPs, 2018.
Heymsfield, A. J. and Donner, L. J.: A scheme for parameterizing ice-cloud
water content in general circulation models, J. Atmos. Sci., 47,
1865–1877, https://doi.org/10.1175/1520-0469(1990)047<1865:ASFPIC>2.0.CO;2, 1990.
HighResMIP: PRIMAVERA-H2020/HighResMIP-futureSSTSeaice, HighResMIP,
available at:
https://github.com/PRIMAVERA-H2020/HighResMIP-futureSSTSeaice, last Access: 17
August 2020.
Hohenegger, C., Kornblueh, L., Klocke, D., Becker, T., Cioni, G., Engels, J.
F., Schulzweida, U., and Stevens, B.: Climate statistics in global
simulations of the atmosphere, from 80 to 2.5 km grid spacing, J. Meteorol.
Soc. Jpn., 98, 73–91, https://doi.org/10.2151/jmsj.2020-005, 2020.
Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A revised approach to ice
microphysical processes for the bulk parameterization of clouds and
precipitation, Mon. Weather Rev., 132, 103–120,
https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2,
2004.
Huffman, G. J., Adler, R. F., Morrissey, M. M., Bolvin, D. T., Curtis, S.,
Joyce, R., McGavock, B., and Susskind, J.: Global Precipitation at One-Degree
Daily Resolution from Multisatellite Observations, J. Hydrometeorol., 2,
36–50, https://doi.org/10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2, 2001.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., and Adler, R. F.: TRMM (TMPA)
Precipitation L3 1 day 0.25 degree x 0.25 degree V7, edited by: Savtchenko, A., Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/TRMM/TMPA/DAY/7,
2016.
Iga, S., Tomita, H., Tsushima, Y., and Satoh, M.: Climatology of a
nonhydrostatic global model with explicit cloud processes, Geophys. Res.
Lett., 34, L22814, https://doi.org/10.1029/2007GL031048, 2007.
Iwasaki, T., Yamada, S., and Tada, K.: A parameterization scheme of
orographic gravity wave drag with two different vertical partitionings Part
I: impacts on medium-range forecasts, J. Meteorol. Soc. Jpn., 67,
11–27, https://doi.org/10.2151/jmsj1965.67.1_11, 1989.
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X., Smith, W. L., Su, W., and Ham, S.-H.: Surface irradiances
of edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy
Balanced and Filled (EBAF) data product, J. Climate, 31, 4501–4527,
https://doi.org/10.1175/JCLI-D-17-0523.1, 2018.
Kennedy, J., Titchner, H., Rayner, N., and Roberts, M.:
input4MIPs.MOHC.SSTsAndSeaIce.HighResMIP.MOHC-HadISST-2-2-0-0-0, Version
20170201, Earth System Grid Federation, https://doi.org/10.22033/ESGF/input4MIPs.1221, 2017.
Kennedy, J., Titchner, H., Rayner, N., and Roberts, M.:
input4MIPs.CMIP6.HighResMIP.MOHC.MOHC-highresSST-future-1-0-0, Version 20190215,
Earth System Grid Federation, https://doi.org/10.22033/ESGF/input4MIPs.10321,
2019.
Kikuchi, K., Kodama, C., Nasuno, T., Nakano, M., Miura, H., Satoh, M., Noda,
A. T., and Yamada, Y.: Tropical intraseasonal oscillation simulated in an
AMIP-type experiment by NICAM, Clim. Dynam., 48, 2507–2528,
https://doi.org/10.1007/s00382-016-3219-z, 2017.
Kilpatrick, T., Xie, S.-P., and Nasuno, T.: Diurnal convection-wind coupling
in the Bay of Bengal, J. Geophys. Res.-Atmos., 122, 9705–9720,
https://doi.org/10.1002/2017JD027271, 2017.
Kinter, J. L., Cash, B., Achuthavarier, D., Adams, J., Altshuler, E.,
Dirmeyer, P., Doty, B., Huang, B., Jin, E. K. K., Marx, L., Manganello, J.,
Stan, C., Wakefield, T., Palmer, T., Hamrud, M., Jung, T., Miller, M.,
Towers, P., Wedi, N., Satoh, M., Tomita, H., Kodama, C., Nasuno, T., Oouchi,
K., Yamada, Y., Taniguchi, H., Andrews, P., Baer, T., Ezell, M., Halloy, C.,
John, D., Loftis, B., Mohr, R., and Wong, K.: Revolutionizing climate
modeling with Project Athena: a multi-institutional, international
collaboration, B. Am. Meteorol. Soc., 94, 231–245,
https://doi.org/10.1175/BAMS-D-11-00043.1, 2013.
Knapp, K. R., Ansari, S., Bain, C. L., Bourassa, M. A., Dickinson, M. J.,
Funk, C., Helms, C. N., Hennon, C. C., Holmes, C. D., Huffman, G. J.,
Kossin, J. P., Lee, H.-T., Loew, A., and Magnusdottir, G.: Globally gridded
satellite observations for climate studies, B. Am. Meteorol. Soc., 92,
893–907, https://doi.org/10.1175/2011BAMS3039.1, 2011.
Knight, C. A., Cooper, W. A., Breed, D. W., Paluch, I. R., Smith, P. L., and
Vali, G.: Microphysics, in Hailstorms of the Central High Plains, edited by:
Knight, C. and Squires, P., Colorado Associated University
Press, 151–193, 1982.
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 reanalysis: general specifications and basic characteristics, J.
Meteorol. Soc. Jpn., 93, 5–48, https://doi.org/10.2151/jmsj.2015-001, 2015.
Kodama, C., Noda, A. T. T., and Satoh, M.: An assessment of the cloud signals
simulated by NICAM using ISCCP, CALIPSO, and CloudSat satellite simulators,
J. Geophys. Res.-Atmos., 117, D12210, https://doi.org/10.1029/2011JD017317, 2012.
Kodama, C., Yamada, Y., Noda, A. T., Kikuchi, K., Kajikawa, Y., Nasuno, T.,
Tomita, T., Yamaura, T., Takahashi, H. G., Hara, M., Kawatani, Y., Satoh,
M., Sugi, M., and Satoh, M.: A 20-year climatology of a NICAM AMIP-type
simulation, J. Meteorol. Soc. Jpn., 93, 393–424,
https://doi.org/10.2151/jmsj.2015-024, 2015.
Kodama, C., Stevens, B., Mauritsen, T., Seiki, T., and Satoh, M.: A new
perspective for future precipitation change from intense extratropical
cyclones, Geophys. Res. Lett., 46, 12435–12444,
https://doi.org/10.1029/2019GL084001, 2019.
Kodama, C., Ohno, T., Seiki, T., Yashiro, H., Noda, A. T., Nakano, M. and Sugi, M.: The non-hydrostatic global atmospheric model for CMIP6 HighResMIP simulations (NICAM16-S) (Version NICAM16-S), Zenodo, https://doi.org/10.5281/zenodo.3727329, 2020.
Lang, S., Tao, W.-K., Simpson, J., Cifelli, R., Rutledge, S., Olson, W., and
Halverson, J.: Improving simulations of convective systems from TRMM LBA:
easterly and westerly Regimes, J. Atmos. Sci., 64, 1141–1164,
https://doi.org/10.1175/JAS3879.1, 2007.
Li, J.-L. F., Forbes, R. M., Waliser, D. E., Stephens, G., and Lee, S.:
Characterizing the radiative impacts of precipitating snow in the ECMWF
Integrated Forecast System global model, J. Geophys. Res.-Atmos., 119,
9626–9637, https://doi.org/10.1002/2014JD021450, 2014.
Li, J.-L. F., Lee, W.-L., Waliser, D., Wang, Y.-H., Yu, J.-Y., Jiang, X.,
L'Ecuyer, T., Chen, Y.-C., Kubar, T., Fetzer, E., and Mahakur, M.:
Considering the radiative effects of snow on tropical Pacific Ocean
radiative heating profiles in contemporary GCMs using A-Train observations,
J. Geophys. Res.-Atmos., 121, 1621–1636, https://doi.org/10.1002/2015JD023587, 2016.
Lin, Y.-L., Farley, R. D., and Orville, H. D.: Bulk Parameterization of the
Snow Field in a Cloud Model, J. Clim. Appl. Meteorol., 22, 1065–1092,
https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2,
1983.
Lindzen, R. S. and Fox-Rabinovitz, M.: Consistent vertical and horizontal
resolution, Mon. Weather Rev., 117, 2575–2583,
https://doi.org/10.1175/1520-0493(1989)117<2575:CVAHR>2.0.CO;2,
1989.
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G.,
Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth's
Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)
Top-of-Atmosphere (TOA) edition-4.0 data product, J. Climate, 31, 895–918,
https://doi.org/10.1175/JCLI-D-17-0208.1, 2018.
Louis, J.-F.: A parametric model of vertical eddy fluxes in the atmosphere,
Bound.-Lay. Meteorol., 17, 187–202, https://doi.org/10.1007/BF00117978, 1979.
LP DAAC: The Land Processes Distributed Active Archive Center (LP DAAC), available at: https://lpdaac.usgs.gov/,
last access: 21 January 2021.
Maher, P., Vallis, G. K., Sherwood, S. C., Webb, M. J., and Sansom, P. G.:
The impact of parameterized convection on climatological precipitation in
atmospheric global climate models, Geophys. Res. Lett., 45, 3728–3736,
https://doi.org/10.1002/2017GL076826, 2018.
Masunaga, H., Matsui, T., Tao, W., Hou, A. Y., Kummerow, C. D., Nakajima,
T., Bauer, P., Olson, W. S., Sekiguchi, M., and Nakajima, T. Y.: Satellite
data simulator unit, B. Am. Meteorol. Soc., 91, 1625–1632,
https://doi.org/10.1175/2010BAMS2809.1, 2010.
Matsugishi, S., Miura, H., Nasuno, T., and Satoh, M.: Impact of latent heat
flux modifications on the reproduction of a Madden–Julian Oscillation event
during the 2015 pre-YMC campaign using a global cloud-system-resolving
model, SOLA, 16A, 12–18,
https://doi.org/10.2151/sola.16A-003, 2020.
Matsui, T., Zeng, X., Tao, W.-K., Masunaga, H., Olson, W. S., and Lang, S.:
Evaluation of long-term cloud-resolving model simulations using satellite
radiance observations and multifrequency satellite simulators, J. Atmos.
Ocean. Tech., 26, 1261–1274, https://doi.org/10.1175/2008JTECHA1168.1, 2009.
Matsuoka, D., Nakano, M., Sugiyama, D., and Uchida, S.: Deep learning
approach for detecting tropical cyclones and their precursors in the
simulation by a cloud-resolving global nonhydrostatic atmospheric model,
Prog. Earth Planet. Sci., 5, 80, https://doi.org/10.1186/s40645-018-0245-y, 2018.
Matthes, K., Funke, B., Kruschke, T., and Wahl, S.:
input4MIPs.SOLARIS-HEPPA.solar.CMIP.SOLARIS-HEPPA-3-2, Version 20170103, Earth System Grid Federation, https://doi.org/10.22033/ESGF/input4MIPs.1122,
2017a.
Matthes, K., Funke, B., Andersson, M. E., Barnard, L., Beer, J., Charbonneau, P., Clilverd, M. A., Dudok de Wit, T., Haberreiter, M., Hendry, A., Jackman, C. H., Kretzschmar, M., Kruschke, T., Kunze, M., Langematz, U., Marsh, D. R., Maycock, A. C., Misios, S., Rodger, C. J., Scaife, A. A., Seppälä, A., Shangguan, M., Sinnhuber, M., Tourpali, K., Usoskin, I., van de Kamp, M., Verronen, P. T., and Versick, S.: Solar forcing for CMIP6 (v3.2), Geosci. Model Dev., 10, 2247–2302, https://doi.org/10.5194/gmd-10-2247-2017, 2017b.
McCoy, D. T., Field, P. R., Elsaesser, G. S., Bodas-Salcedo, A., Kahn, B. H., Zelinka, M. D., Kodama, C., Mauritsen, T., Vanniere, B., Roberts, M., Vidale, P. L., Saint-Martin, D., Voldoire, A., Haarsma, R., Hill, A., Shipway, B., and Wilkinson, J.: Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations, Atmos. Chem. Phys., 19, 1147–1172, https://doi.org/10.5194/acp-19-1147-2019, 2019.
McFarlane, N. A.: The effect of orographically excited gravity wave drag on
the general circulation of the lower stratosphere and troposphere, J. Atmos.
Sci., 44, 1775–1800, https://doi.org/10.1175/1520-0469(1987)044<1775:TEOOEG>2.0.CO;2, 1987.
McFarlane, N. A., Boer, G. J., Blanchet, J.-P., and Lazare, M.: The Canadian
Climate Centre second-generation general circulation model and its
equilibrium climate, J. Climate, 5, 1013–1044,
https://doi.org/10.1175/1520-0442(1992)005<1013:TCCCSG>2.0.CO;2,
1992.
Meinshausen, M. and Nicholls, Z. R. J.: UoM-REMIND-MAGPIE-ssp585-1-2-1 GHG
concentrations, Version 20181127, Earth System Grid Federation. https://doi.org/10.22033/ESGF/input4MIPs.9868, 2018.
Meinshausen, M. and Vogel, E.:
input4MIPs.UoM.GHGConcentrations.CMIP.UoM-CMIP-1-2-0, Version 20160830,
Earth System Grid Federation, https://doi.org/10.22033/ESGF/input4MIPs.1118,
2016.
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017.
Michibata, T., Suzuki, K., Sekiguchi, M., and Takemura, T.: Prognostic
precipitation in the MIROC6-SPRINTARS GCM: description and evaluation
against satellite observations, J. Adv. Model. Earth Sy., 11, 839–860,
https://doi.org/10.1029/2018MS001596, 2019.
Mitchell, D. L.: Use of mass- and area-dimensional power laws for
determining precipitation particle terminal velocities, J. Atmos. Sci.,
53, 1710–1723, https://doi.org/10.1175/1520-0469(1996)053<1710:UOMAAD>2.0.CO;2, 1996.
Miyakawa, T. and Miura, H.: Resolution dependencies of tropical convection
in a global cloud/cloud-system resolving model, J. Meteorol. Soc. Jpn.,
97, 745–756, https://doi.org/10.2151/jmsj.2019-034, 2019.
Miyakawa, T., Yashiro, H., Suzuki, T., Tatebe, H., and Satoh, M.: A Madden-Julian Oscillation event remotely accelerates ocean upwelling to abruptly terminate the 1997/1998 super El Niño, Geophys. Res. Lett., 44, 9489–9495, https://doi.org/10.1002/2017GL074683, 2017.
Miyakawa, T., Noda, A. T., and Kodama, C.: The impact of hybrid usage of a
cumulus parameterization scheme on tropical convection and large-scale
circulations in a global cloud-system resolving model, J. Adv. Model. Earth
Sy., 10, 2952–2970, https://doi.org/10.1029/2018MS001302, 2018.
Moon, I.-J., Ginis, I., Hara, T., and Thomas, B.: A physics-based
parameterization of air–sea momentum flux at high wind speeds and its
impact on hurricane intensity predictions, Mon. Weather Rev., 135,
2869–2878, https://doi.org/10.1175/MWR3432.1, 2007.
Na, Y., Fu, Q., and Kodama, C.: Precipitation probability and its future
changes from a global cloud-resolving model and CMIP6 simulations, J.
Geophys. Res.-Atmos., 125, e2019JD031926, https://doi.org/10.1029/2019JD031926, 2020.
Nakajima, T., Tsukamoto, M., Tsushima, Y., Numaguti, A., and Kimura, T.:
Modeling of the radiative process in an atmospheric general circulation
model, Appl. Optics, 39, 4869–4878, 2000.
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada level-3 model: Its
numerical stability and application to a regional prediction of advection
fog, Bound.-Lay. Meteorol., 119, 397–407,
https://doi.org/10.1007/s10546-005-9030-8, 2006.
Nakano, M. and Kikuchi, K.: Seasonality of intraseasonal variability in
global climate models, Geophys. Res. Lett., 46, 4441–4449,
https://doi.org/10.1029/2019GL082443, 2019.
Nappo, C.: An introduction to atmospheric gravity waves, 2nd Edn., Academic
Press, Cambridge, MA, 2012.
Nitta, T., Yoshimura, K. and Abe-Ouchi, A.: Impact of Arctic Wetlands on the
Climate System: Model Sensitivity Simulations with the MIROC5 AGCM and a
Snow-Fed Wetland Scheme, J. Hydrometeorol., 18, 2923–2936,
https://doi.org/10.1175/JHM-D-16-0105.1, 2017.
Niwano, M., Aoki, T., Kuchiki, K., Hosaka, M., Kodama, Y., Yamaguchi, S.,
Moytoyoshi, H., and Iwata, Y.: Evaluation of updated physical snowpack model
SMAP, Bull. Glaciol. Res., 32, 65–78, https://doi.org/10.5331/bgr.32.65, 2014.
Noda, A. T., Oouchi, K., Satoh, M., Tomita, H., Iga, S., and Tsushima, Y.:
Importance of the subgrid-scale turbulent moist process: Cloud distribution
in global cloud-resolving simulations, Atmos. Res., 96, 208–217,
https://doi.org/10.1016/j.atmosres.2009.05.007, 2010.
Noda, A. T., Oouchi, K., Satoh, M., and Tomita, H.: Quantitative assessment
of diurnal variation of tropical convection simulated by a global
nonhydrostatic model without cumulus parameterization, J. Climate, 25,
5119–5134, https://doi.org/10.1175/JCLI-D-11-00295.1, 2012.
Noda, A. T., Seiki, T., Satoh, M., and Yamada, Y.: High cloud size dependency
in the applicability of the fixed anvil temperature hypothesis using global
nonhydrostatic simulations, Geophys. Res. Lett., 43, 2307–2314, https://doi.org/10.1002/2016GL067742,
2016.
Noda, A. T., Kodama, C., Yamada, Y., Satoh, M., Ogura, T., and Ohno, T.:
Responses of clouds and large-scale circulation to global warming evaluated
from multidecadal simulations using a global nonhydrostatic model, J. Adv.
Model. Earth Sy., 11, 2980–2995, https://doi.org/10.1029/2019MS001658, 2019.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Ohno, T., Satoh, M., and Noda, A.: Fine vertical resolution
radiative-convective equilibrium experiments: roles of turbulent mixing on
the high-cloud response to sea surface temperatures, J. Adv. Model. Earth
Sy., 11, 1637–1654, https://doi.org/10.1029/2019MS001704, 2019.
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart,
F., Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N.,
Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher, M.:
ERA-20C: an atmospheric reanalysis of the twentieth century, J. Climate,
29, 4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1, 2016.
Polichtchouk, I., Stockdale, T., Bechtold, P., Diamantakis, M., Malardel,
S., Sandu, I., Vána, F., and Wedi, N.: Control on stratospheric
temperature in IFS: resolution and vertical advection, ECMWF Tech. Memo.,
847, https://doi.org/10.21957/cz3t12t7e, 2019.
Roh, W. and Satoh, M.: Evaluation of precipitating hydrometeor
parameterizations in a single-moment bulk microphysics scheme for deep
convective systems over the tropical central Pacific, J. Atmos. Sci., 71,
2654–2673, https://doi.org/10.1175/JAS-D-13-0252.1, 2014.
Roh, W. and Satoh, M.: Extension of a multisensor satellite radiance-based
evaluation for cloud system resolving models, J. Meteorol. Soc. Jpn.,
96, 55–63, https://doi.org/10.2151/jmsj.2018-002, 2018.
Roh, W., Satoh, M., and Nasuno, T.: Improvement of a cloud microphysics
scheme for a global nonhydrostatic model using TRMM and a satellite
simulator, J. Atmos. Sci., 74, 167–184, https://doi.org/10.1175/JAS-D-16-0027.1,
2017.
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287,
https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2,
1999.
Rutledge, S. A. and Hobbs, P. V.: The mesoscale and microscale structure and
organization of clouds and precipitation in midlatitude cyclones. XII: a
diagnostic modeling study of precipitation development in narrow
cold-frontal rainbands, J. Atmos. Sci., 41, 2949–2972,
https://doi.org/10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2,
1984.
Sato, T., Miura, H., Satoh, M., Takayabu, Y. N., and Wang, Y.: Diurnal cycle
of precipitation in the tropics simulated in a global cloud-resolving model,
J. Climate, 22, 4809–4826, https://doi.org/10.1175/2009JCLI2890.1, 2009.
Sato, Y., Goto, D., Michibata, T., Suzuki, K., Takemura, T., Tomita, H., and
Nakajima, T.: Aerosol effects on cloud water amounts were successfully
simulated by a global cloud-system resolving model, Nat. Commun., 9, 985,
https://doi.org/10.1038/s41467-018-03379-6, 2018.
Satoh, M., Matsuno, T., Tomita, H., Miura, H., Nasuno, T., and Iga, S.:
Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud
resolving simulations, J. Comput. Phys., 227, 3486–3514,
https://doi.org/10.1016/j.jcp.2007.02.006, 2008.
Satoh, M., Inoue, T., and Miura, H.: Evaluations of cloud properties of
global and local cloud system resolving models using CALIPSO and CloudSat
simulators, J. Geophys. Res., 115, D00H14, https://doi.org/10.1029/2009JD012247, 2010.
Satoh, M., Tomita, H., Yashiro, H., Miura, H., Kodama, C., Seiki, T., Noda,
A. T., Yamada, Y., Goto, D., Sawada, M., Miyoshi, T., Niwa, Y., Hara, M.,
Ohno, T., Iga, S., Arakawa, T., Inoue, T., and Kubokawa, H.: The
non-hydrostatic icosahedral atmospheric model: Description and development,
Prog. Earth Planet. Sci., 1, 18, https://doi.org/10.1186/s40645-014-0018-1, 2014.
Satoh, M., Yamada, Y., Sugi, M., Kodama, C., and Noda, A. T. T.: Constraint
on future change in global frequency of tropical cyclones due to global
warming, J. Meteorol. Soc. Jpn., 93, 489–500, https://doi.org/10.2151/jmsj.2015-025,
2015.
Satoh, M., Noda, A. T., Seiki, T., Chen, Y.-W., Kodama, C., Yamada, Y.,
Kuba, N., and Sato, Y.: Toward reduction of the uncertainties in climate
sensitivity due to cloud processes using a global non-hydrostatic
atmospheric model, Prog. Earth Planet. Sci., 5, 67,
https://doi.org/10.1186/s40645-018-0226-1, 2018.
Satoh, M., Stevens, B., Judt, F., Khairoutdinov, M., Lin, S.-J., Putman, W.
M., and Düben, P.: Global cloud-resolving models, Curr. Clim. Chang.
Reports, 5, 172–184, https://doi.org/10.1007/s40641-019-00131-0, 2019.
Seiki, T. and Nakajima, T.: Aerosol effects of the condensation process on a
convective cloud simulation, J. Atmos. Sci., 71, 833–853,
https://doi.org/10.1175/JAS-D-12-0195.1, 2014.
Seiki, T., Satoh, M., Tomita, H., and Nakajima, T.: Simultaneous evaluation
of ice cloud microphysics and nonsphericity of the cloud optical properties
using hydrometeor video sonde and radiometer sonde in situ observations, J.
Geophys. Res.-Atmos., 119, 6681–6701, https://doi.org/10.1002/2013JD021086, 2014.
Seiki, T., Kodama, C., Noda, A. T. and Satoh, M.: Improvement in global
cloud-system-resolving simulations by using a double-moment bulk cloud
microphysics scheme, J. Climate, 28, 2405–2419,
https://doi.org/10.1175/JCLI-D-14-00241.1, 2015a.
Seiki, T., Kodama, C., Satoh, M., Hashino, T., Hagihara, Y., and Okamoto, H.:
Vertical grid spacing necessary for simulating tropical cirrus clouds with a
high-resolution atmospheric general circulation model, Geophys. Res. Lett.,
42, 4150–4157, https://doi.org/10.1002/2015GL064282, 2015b.
Sekiguchi, M. and Nakajima, T.: A k-distribution-based radiation code and
its computational optimization for an atmospheric general circulation model,
J. Quant. Spectrosc. Ra., 109, 2779–2793,
https://doi.org/10.1016/j.jqsrt.2008.07.013, 2008.
Shabanov, N. V., Huang, D., Yang, W., Tan, B., Knyazikhin, Y., Myneni, R.
B., Ahl, D. E., Gower, S. T., Huete, A. R., Aragao, L. E. O. C., and
Shimabukuro, Y. E.: Analysis and optimization of the MODIS leaf area index
algorithm retrievals over broadleaf forests, IEEE T. Geosci. Remote, 43, 1855–1865, https://doi.org/10.1109/TGRS.2005.852477, 2005.
Skamarock, W. C., Snyder, C., Klemp, J. B., and Park, S.-H.: Vertical
Resolution Requirements in Atmospheric Simulation, Mon. Weather Rev.,
147, 2641–2656, https://doi.org/10.1175/MWR-D-19-0043.1, 2019.
Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433–452, https://doi.org/10.5194/gmd-10-433-2017, 2017.
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C., Düben,
P., Judt, F., Khairoutdinov, M., Klocke, D., Kornblueh, L., Kodama, C.,
Neumann, P., Lin, S., Putman, W. M., Röber, N., Shibuya, R., Vidale, P.,
and Wedi, N.: DYAMOND: The DYnamics of the Atmospheric general circulation
Modeled On Non-hydrostatic Domains, Prog. Earth Planet. Sci., 6, 1–18, https://doi.org/10.1186/s40645-019-0304-z, 2019.
Sugi, M., Yamada, Y., Yoshida, K., Mizuta, R., Nakano, M., Kodama, C., and
Satoh, M.: Future changes in the global frequency of tropical cyclone seeds,
SOLA, 16, 70–74, https://doi.org/10.2151/sola.2020-012, 2020.
Suzuki, K., Nakajima, T., Satoh, M., Tomita, H., Takemura, T., Nakajima, T.
Y., and Stephens, G. L.: Global cloud-system-resolving simulation of aerosol
effect on warm clouds, Geophys. Res. Lett., 35, L19817,
https://doi.org/10.1029/2008GL035449, 2008.
Takahashi, H. G., Kamizawa, N., Nasuno, T., Yamada, Y., Kodama, C.,
Sugimoto, S., and Satoh, M.: Response of the Asian Summer Monsoon
Precipitation to Global Warming in a High-Resolution Global Nonhydrostatic
Model, J. Climate, 33, 8147–8164, https://doi.org/10.1175/JCLI-D-19-0824.1, 2020.
Takasuka, D., Miyakawa, T., Satoh, M., and Miura, H.: Topographical effects
on internally produced MJO-like disturbances in an aqua-planet version of
NICAM, SOLA, 11, 170–176, https://doi.org/10.2151/sola.2015-038, 2015.
Takasuka, D., Satoh, M., Miyakawa, T., and Miura, H.: Initiation processes of
the tropical intraseasonal variability simulated in an aqua-planet
experiment: what is the intrinsic mechanism for MJO onset?, J. Adv. Model.
Earth Sy., 10, 1047–1073, https://doi.org/10.1002/2017MS001243, 2018.
Takata, K., Emori, S., and Watanabe, T.: Development of the minimal advanced
treatments of surface interaction and runoff, Glob. Planet. Change,
38, 209–222, https://doi.org/10.1016/S0921-8181(03)00030-4, 2003.
Takemura, T., Okamoto, H., Maruyama, Y., Numaguti, A., Higurashi, A., and
Nakajima, T.: Global three-dimensional simulation of aerosol optical
thickness distribution of various origins, J. Geophys. Res.-Atmos.,
105, 17853–17873, https://doi.org/10.1029/2000JD900265, 2000.
Takemura, T., Nakajima, T., Dubovik, O., Holben, B. N., and Kinne, S.:
Single-scattering albedo and radiative forcing of various aerosol species
with a global three-dimensional model, J. Climate, 15, 333–352,
https://doi.org/10.1175/1520-0442(2002)015<0333:SSAARF>2.0.CO;2,
2002.
Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y., and Nakajima, T.:
Simulation of climate response to aerosol direct and indirect effects with
aerosol transport-radiation model, J. Geophys. Res., 110, D02202,
https://doi.org/10.1029/2004JD005029, 2005.
Takemura, T., Egashira, M., Matsuzawa, K., Ichijo, H., O'ishi, R., and Abe-Ouchi, A.: A simulation of the global distribution and radiative forcing of soil dust aerosols at the Last Glacial Maximum, Atmos. Chem. Phys., 9, 3061–3073, https://doi.org/10.5194/acp-9-3061-2009, 2009.
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019.
Thomason, L., Vernier, J.-P., Bourassa, A., Arfeuille, F., Bingen, C.,
Peter, T., and Luo, B.: Stratospheric Aerosol Data Set (SADS Version 2)
prospectus, available at:
http://www.wcrp-climate.org/images/modelling/WGCM/CMIP/CMIP6Forcings_StratosphericAerosolDataSet_InitialDescription_150131.pdf (last access: 28 July 2020), 2015.
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, https://doi.org/10.1175/2008MWR2387.1, 2008.
Tomita, H.: New microphysical schemes with five and six categories by
diagnostic generation of cloud ice, J. Meteorol. Soc. Jpn., 86A, 121–142,
https://doi.org/10.2151/jmsj.86A.121, 2008.
Tomita, H. and Satoh, M.: A new dynamical framework of nonhydrostatic global
model using the icosahedral grid, Fluid Dyn. Res., 34, 357–400,
https://doi.org/10.1016/j.fluiddyn.2004.03.003, 2004.
Tomita, H., Tsugawa, M., Satoh, M., and Goto, K.: Shallow water model on a
modified icosahedral geodesic grid by using spring dynamics, J. Comput.
Phys., 174, 579–613, https://doi.org/10.1006/jcph.2001.6897, 2001.
Tomita, H., Satoh, M., and Goto, K.: An optimization of the icosahedral grid
modified by spring dynamics, J. Comput. Phys., 183, 307–331,
https://doi.org/10.1006/jcph.2002.7193, 2002.
USGS EROS Archive: Digital Elevation – Global 30 Arc-Second Elevation (GTOPO30),
https://doi.org/10.5066/F7DF6PQS, 2021.
Waliser, D. E., Li, J.-L. F., L'Ecuyer, T. S., and Chen, W.-T.: The impact of
precipitating ice and snow on the radiation balance in global climate
models, Geophys. Res. Lett., 38, L06802, https://doi.org/10.1029/2010GL046478, 2011.
Watanabe, S., Sato, K., Kawatani, Y., and Takahashi, M.: Vertical resolution dependence of gravity wave momentum flux simulated by an atmospheric general circulation model, Geosci. Model Dev., 8, 1637–1644, https://doi.org/10.5194/gmd-8-1637-2015, 2015.
Williams, K. D., Bodas-Salcedo, A., Déqué, M., Fermepin, S., Medeiros, B., Watanabe, M., Jakob, C., Klein, S. A., Senior, C. A., and Williamson, D. L.: The Transpose-AMIP II Experiment and Its Application to the Understanding of Southern Ocean Cloud Biases in Climate Models, J. Climate, 26, 3258–3274, https://doi.org/10.1175/JCLI-D-12-00429.1, 2013.
WCRP: input4MIPs, available at: https://esgf-node.llnl.gov/projects/input4mips/,
last access: 21 January 2021.
Yamada, Y., Satoh, M., Sugi, M., Kodama, C., Noda, A. T., Nakano, M., and
Nasuno, T.: Response of tropical cyclone activity and structure to global
warming in a high-resolution global nonhydrostatic model, J. Climate, 30, 9703–9724, https://doi.org/10.1175/JCLI-D-17-0068.1, 2017.
Yamada, Y., Kodama, C., Satoh, M., Nakano, M., Nasuno, T., and Sugi, M.:
High-resolution ensemble simulations of intense tropical cyclones and their
internal variability during the El Niños of 1997 and 2015, Geophys. Res.
Lett., 46, 7592–7601, https://doi.org/10.1029/2019GL082086, 2019.
Yamazaki, T., Taguchi, B., and Kondo, J.: Estimation of the heat balance in a
small snow-covered forested catchment basin, Tenki, 41,
71–77, 1994 (in Japanese).
Yang, W., Tan, B., Huang, D., Rautiainen, M., Shabanov, N. V., Wang, Y.,
Privette, J. L., Huemmrich, K. F., Fensholt, R., Sandholt, I., Weiss, M.,
Ahl, D. E., Gower, S. T., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B.:
MODIS leaf area index products: from validation to algorithm improvement,
IEEE T. Geosci. Remote, 44, 1885–1898,
https://doi.org/10.1109/TGRS.2006.871215, 2006.
Yashiro, H., Terai, M., Yoshida, R., Iga, S., Minami, K., and Tomita, H.:
Performance analysis and optimization of Nonhydrostatic ICosahedral
Atmospheric Model (NICAM) on the K Computer and TSUBAME2.5, in: Proceedings
of the Platform for Advanced Scientific Computing Conference on PASC '16,
ACM Press, New York, New York, USA, 1–8, https://doi.org/10.1145/2929908.2929911, 2016.
Yoshizaki, M., Iga, S., and Satoh, M.: Eastward-propagating property of
large-scale precipitation systems simulated in the coarse-resolution NICAM
and an explanation of its appearance, SOLA, 8, 21–24,
https://doi.org/10.2151/sola.2012-006, 2012.
Short summary
This paper describes the latest stable version of NICAM, a global atmospheric model, developed for high-resolution climate simulations toward the IPCC Assessment Report. Our model explicitly treats convection, clouds, and precipitation and could reduce the uncertainty of climate change projection. A series of test simulations demonstrated improvements (e.g., high cloud) and issues (e.g., low cloud, precipitation pattern), suggesting further necessity for model improvement and higher resolutions.
This paper describes the latest stable version of NICAM, a global atmospheric model, developed...
Special issue