Articles | Volume 12, issue 10
https://doi.org/10.5194/gmd-12-4297-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4297-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
10 Oct 2019
Development and technical paper |
| 10 Oct 2019
| 10 Oct 2019
Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation
Takuro Michibata
CORRESPONDING AUTHOR
Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
Kentaroh Suzuki
Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan
Tomoo Ogura
Center for Global Environmental Research, National Institute for Environmental Studies, Ibaraki, Japan
Xianwen Jing
Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan
now at: Department of Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan, USA
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Single-layer warm liquid clouds cover nearly one-third of the Earth's surface, and uncertainties regarding the impact of aerosols on their radiative properties pose a significant challenge to climate prediction. Here, we demonstrate how satellite observations can be used to constrain Earth system model estimates of the radiative forcing from the interactions of aerosols with clouds due to warm rain processes.
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Revised manuscript not accepted
T. Michibata, K. Kawamoto, and T. Takemura
Atmos. Chem. Phys., 14, 11935–11948, https://doi.org/10.5194/acp-14-11935-2014, https://doi.org/10.5194/acp-14-11935-2014, 2014
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Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
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The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
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The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
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There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
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This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
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We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
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This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
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Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
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In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
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We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
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For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
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Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
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This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
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HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
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This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
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An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
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This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
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Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
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Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
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Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
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Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
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This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
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Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
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Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
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Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
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Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
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An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
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Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
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In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
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The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
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In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
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The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
EGUsphere, https://doi.org/10.22541/essoar.169903618.82717612/v2, https://doi.org/10.22541/essoar.169903618.82717612/v2, 2024
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Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate of the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
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In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
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Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
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This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
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PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Kelly M. Núñez Ocasio and Zachary L. Moon
EGUsphere, https://doi.org/10.5194/egusphere-2024-259, https://doi.org/10.5194/egusphere-2024-259, 2024
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TAMS is an open-source mesoscale convective system tracking and classifying Python-based package that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Cited articles
Bai, H., Gong, C., Wang, M., Zhang, Z., and L'Ecuyer, T.: Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites, Atmos. Chem. Phys., 18, 1763–1783, https://doi.org/10.5194/acp-18-1763-2018, 2018. a
Beheng, K. D.: A parameterization of warm cloud microphysical conversion
processes, Atmos. Res., 33, 193–206, 1994. a
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. a, b
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh,
S. K., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and aerosols,
Cambridge University Press, Cambridge, UK,
571–657, https://doi.org/10.1017/CBO9781107415324.016, 2013. a
Brenguier, J.-L., Pawlowska, H., Schüller, L., Preusker, R., Fischer, J.,
and Fouquart, Y.: Radiative Properties of Boundary Layer Clouds: Droplet
Effective Radius versus Number Concentration, J. Atmos.
Sci., 57, 803–821,
https://doi.org/10.1175/1520-0469(2000)057<0803:RPOBLC>2.0.CO;2, 2000. a
Bretherton, C. S.: Insights into low-latitude cloud feedbacks from
high-resolution models, Philos. T. Roy. Soc. A, 373, 20140415,
https://doi.org/10.1098/rsta.2014.0415, 2015. a
Cesana, G. and Chepfer, H.: How well do climate models simulate cloud vertical
structure? A comparison between CALIPSO-GOCCP satellite observations and
CMIP5 models, Geophys. Res. Lett., 39, 1–6,
https://doi.org/10.1029/2012GL053153, 2012. a
Chen, J., Liu, Y., Zhang, M., and Peng, Y.: New understanding and
quantification of the regime dependence of aerosol-cloud interaction for
studying aerosol indirect effects, Geophys. Res. Lett., 43,
1780–1787, https://doi.org/10.1002/2016GL067683, 2016. a
Chen, J., Liu, Y., Zhang, M., and Peng, Y.: Height Dependency of Aerosol-Cloud
Interaction Regimes, J. Geophys. Res.-Atmos., 123,
491–506, https://doi.org/10.1002/2017JD027431, 2018. a
Chen, Y.-C., Christensen, M. W., Stephens, G. L., and Seinfeld, J. H.:
Satellite-based estimate of global aerosol-cloud radiative forcing by marine
warm clouds, Nat. Geosci., 7, 643–646, https://doi.org/10.1038/ngeo2214, 2014. a
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. a
Chikira, M. and Sugiyama, M.: A cumulus parameterization with state-dependent
entrainment rate. Part I: Description and sensitivity to temperature and
humidity profiles, J. Atmos. Sci., 67, 2171–2193,
https://doi.org/10.1175/2010JAS3316.1, 2010. a
Christensen, M. W., Chen, Y. C., and Stephens, G. L.: Aerosol indirect effect
dictated by liquid clouds, J. Geophys. Res., 121,
14636–14650, https://doi.org/10.1002/2016JD025245, 2016. a
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. a
Gettelman, A. and Sherwood, S. C.: Processes Responsible for Cloud Feedback,
Current Climate Change Reports, 179–189,
https://doi.org/10.1007/s40641-016-0052-8, 2016. a
Gryspeerdt, E. and Stier, P.: Regime-based analysis of aerosol-cloud
interactions, Geophys. Res. Lett., 39, L21802,
https://doi.org/10.1029/2012GL053221, 2012. a
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, 1723–1727,
https://doi.org/10.1175/BAMS-88-11-1723, 2007. a
Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C.,
Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with
spaceborne W-band radar, J. Geophys. Res.-Atmos., 114,
D00A22, https://doi.org/10.1029/2008JD009973, 2009. a, b
Huang, L., Jiang, J. H., Wang, Z., Su, H., Deng, M., and Massie, S.:
Climatology of cloud water content associated with different cloud types
observed by A-Train satellites, J. Geophys. Res.-Atmos., 120, 4196–4212, https://doi.org/10.1002/2014JD022779, 2015. a
Jing, X., Suzuki, K., Guo, H., Goto, D., Ogura, T., Koshiro, T., and
Mülmenstädt, J.: A Multimodel Study on Warm Precipitation Biases
in Global Models Compared to Satellite Observations, J. Geophys.
Res.-Atmos., 122, 11806–11824, https://doi.org/10.1002/2017JD027310, 2017. a
Jing, X., Suzuki, K., and Michibata, T.: The Key Role of Warm Rain
Parameterization in Determining the Aerosol Indirect Effect in a Global
Climate Model, J. Climate, 32, 4409–4430,
https://doi.org/10.1175/jcli-d-18-0789.1, 2019. a
Kay, J. E., L'Ecuyer, T., Pendergrass, A., Chepfer, H., Guzman, R., and
Yettella, V.: Scale-Aware and Definition-Aware Evaluation of Modeled
Near-Surface Precipitation Frequency Using CloudSat Observations, J.
Geophys. Res.-Atmos., 123, 4294–4309, https://doi.org/10.1002/2017JD028213, 2018. a, b, c, d, e
Khairoutdinov, M. and Kogan, Y.: A new cloud physics parameterization in a
large-eddy simulation model of marine stratocumulus, Mon. Weather
Rev., 128, 229–243, 2000. a
Kikuchi, M., Okamoto, H., Sato, K., Suzuki, K., Cesana, G., Hagihara, Y.,
Takahashi, N., Hayasaka, T., and Oki, R.: Development of Algorithm for
Discriminating Hydrometeor Particle Types with a Synergistic Use of CloudSat
and CALIPSO, J. Geophys. Res.-Atmos., 122,
11022–11044, https://doi.org/10.1002/2017JD027113, 2017. a
Klein, S. A. and Jakob, C.: Validation and sensitivities of frontal clouds
simulated by the ECMWF model, Mon. Weather Rev., 127, 2514–2531,
https://doi.org/10.1175/1520-0493(1999)127<2514:vasofc>2.0.co;2, 1999. a
Konsta, D., Dufresne, J. L., Chepfer, H., Idelkadi, A., and Cesana, G.: Use of A-train satellite observations (CALIPSO–PARASOL) to evaluate tropical
cloud properties in the LMDZ5 GCM, Clim. Dynam., 47, 1263–1284,
https://doi.org/10.1007/s00382-015-2900-y, 2016. a, b
Kubar, T. L., Hartmann, D. L., and Wood, R.: Understanding the Importance of
Microphysics and Macrophysics for Warm Rain in Marine Low Clouds. Part I:
Satellite Observations, J. Atmos. Sci., 66, 2953–2972,
https://doi.org/10.1175/2009JAS3071.1, 2009. a
Lebsock, M. D., Stephens, G. L., and Kummerow, C.: Multisensor satellite
observations of aerosol effects on warm clouds, J. Geophys. Res., 113, D15205, https://doi.org/10.1029/2008JD009876, 2008. a
L'Ecuyer, T. S. and Jiang, J. H.: Touring the atmosphere aboard the A-Train,
Phys. Today, 63, 36–41, https://doi.org/10.1063/1.3653856, 2010. a
L'Ecuyer, T. S., Beaudoing, H. K., Rodell, M., Olson, W., Lin, B., Kato, S.,
Clayson, C. A., Wood, E., Sheffield, J., Adler, R., Huffman, G., Bosilovich,
M., Gu, G., Robertson, F., Houser, P. R., Chambers, D., Famiglietti, J. S.,
Fetzer, E., Liu, W. T., Gao, X., Schlosser, C. A., Clark, E., Lettenmaier,
D. P., and Hilburn, K.: The observed state of the energy budget in the early
21st century, J. Climate, 28, 8319–8346,
https://doi.org/10.1175/JCLI-D-14-00556.1, 2015. a
Leon, D. C., Wang, Z., and Liu, D.: Climatology of drizzle in marine boundary
layer clouds based on 1 year of data from CloudSat and Cloud-Aerosol Lidar
and Infrared Pathfinder Satellite Observations (CALIPSO), J. Geophys. Res., 113, D00A14, https://doi.org/10.1029/2008JD009835, 2008. a
Ma, P. L., Rasch, P. J., Chepfer, H., Winker, D. M., and Ghan, S. J.:
Observational constraint on cloud susceptibility weakened by aerosol
retrieval limitations, Nat. Commun., 9, 2640,
https://doi.org/10.1038/s41467-018-05028-4, 2018. a
Mace, G. G., Marchand, R., Zhang, Q., and Stephens, G.: Global hydrometeor
occurrence as observed by CloudSat: Initial observations from summer 2006,
Geophys. Res. Lett., 34, L09808, https://doi.org/10.1029/2006GL029017, 2007. a
Maloney, E. D., Gettelman, A., Ming, Y., Neelin, J. D., Barrie, D., Mariotti,
A., Chen, C.-C., Coleman, D. R. B., Kuo, Y.-H., Singh, B., Annamalai, H.,
Berg, A., Booth, J. F., Camargo, S. J., Dai, A., Gonzalez, A., Hafner, J.,
Jiang, X., Jing, X., Kim, D., Kumar, A., Moon, Y., Naud, C. M., Sobel, A. H.,
Suzuki, K., Wang, F., Wang, J., Wing, A. A., Xu, X., and Zhao, M.:
Process-Oriented Evaluation of Climate and Weather Forecasting Models,
B. Am. Meteorol. Soc., 100, 1665–1686,
https://doi.org/10.1175/bams-d-18-0042.1, 2019. a
Marchand, R. and Ackerman, T.: An analysis of cloud cover in multiscale
modeling framework global climate model simulations using 4 and 1 km
horizontal grids, J. Geophys. Res.-Atmos., 115,
D16207, https://doi.org/10.1029/2009JD013423, 2010. a
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor
detection using CloudSat – An Earth-orbiting 94-GHz cloud radar, J.
Atmos. Ocean. Tech., 25, 519–533,
https://doi.org/10.1175/2007JTECHA1006.1, 2008. a
Marchand, R., Haynes, J., Mace, G. G., Ackerman, T., and Stephens, G.: A
comparison of simulated cloud radar output from the multiscale modeling
framework global climate model with CloudSat cloud radar observations,
J. Geophys. Res., 114, D00A20, https://doi.org/10.1029/2008JD009790, 2009. a
Masunaga, H., Matsui, T., Tao, W.-K., Hou, A. Y., Kummerow, C. D., Nakajima,
T., Bauer, P., Olson, W. S., Sekiguchi, M., and Nakajima, T. Y.: Satellite
Data Simulator Unit A multisensor, multispectral Satellite Simulator
Package, B. Am. Meteorol. Soc., 91, 1625–1632,
https://doi.org/10.1175/2010BAMS2809.1, 2010. a
Matus, A. V. and L'Ecuyer, T. S.: The role of cloud phase in Earth's radiation
budget, J. Geophys. Res.-Atmos., 122, 1–20,
https://doi.org/10.1002/2016JD025951, 2017. a
Medeiros, B. and Stevens, B.: Revealing differences in GCM representations of
low clouds, Clim. Dynam., 36, 385–399, https://doi.org/10.1007/s00382-009-0694-5,
2011. a
Michibata, T. and Takemura, T.: Evaluation of autoconversion schemes in a
single model framework with satellite observations, J. Geophys. Res., 120, 9570–9590, https://doi.org/10.1002/2015JD023818, 2015. a
Michibata, T., Kawamoto, K., and Takemura, T.: The effects of aerosols on water cloud microphysics and macrophysics based on satellite-retrieved data over East Asia and the North Pacific, Atmos. Chem. Phys., 14, 11935–11948, https://doi.org/10.5194/acp-14-11935-2014, 2014. a, b, c
Michibata, T., Suzuki, K., Sato, Y., and Takemura, T.: The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals, Atmos. Chem. Phys., 16, 15413–15424, https://doi.org/10.5194/acp-16-15413-2016, 2016. a, b, c, d
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, 2019a. a
Michibata, T., Suzuki, K., Ogura, T., and Jing, X.: Scripts for the publication “Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation” (Version 1.0.0), Zenodo, https://doi.org/10.5281/zenodo.1442468, 2019b. a, b
Michibata, T., Suzuki, K., Ogura, T., and Jing, X.: Data for the publication “Incorporation of inline warm rain diagnostics into the COSP2 satellite simulator for process-oriented model evaluation” (Version 1.0.0), Zenodo, https://doi.org/10.5281/zenodo.3370823, 2019c. a, b
Mülmenstädt, J. and Feingold, G.: The Radiative Forcing of
Aerosol–Cloud Interactions in Liquid Clouds: Wrestling and Embracing
Uncertainty, Current Climate Change Reports, 4, 23–40,
https://doi.org/10.1007/s40641-018-0089-y, 2018. a
Mülmenstädt, J., Sourdeval, O., Delanoë, J., and Quaas, J.:
Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds
derived from A-Train satellite retrievals, Geophys. Res. Lett., 42,
6502–6509, https://doi.org/10.1002/2015GL064604, 2015. a
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang,
J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock,
A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and natural
radiative forcing, Cambridge University Press, Cambridge, UK,
659–740, https://doi.org/10.1017/CBO9781107415324.018, 2013. a
Nakajima, T. Y., Suzuki, K., and Stephens, G. L.: Droplet Growth in Warm Water
Clouds Observed by the A-Train. Part II: A Multisensor View, J.
Atmos. Sci., 67, 1897–1907, https://doi.org/10.1175/2010JAS3276.1, 2010. a, b
Nam, C. C. W., Johannes Quaas, Neggers, R., Drian, C. S.-L., and Isotta, F.:
Evaluation of boundary layer cloud parameterizations in the ECHAM5 general
circulationmodel using CALIPSO and CloudSat satellite data, J.
Adv. Model. Earth Sy., 6, 300–314,
https://doi.org/10.1002/2013MS000277, 2014. a
Park, S. and Bretherton, C. S.: The University of Washington shallow
convection and moist turbulence schemes and their impact on climate
simulations with the community atmosphere model, J. Climate, 22,
3449–3469, https://doi.org/10.1175/2008JCLI2557.1, 2009. a
Partain, P.: Cloudsat ECMWF-AUX auxiliary data process description and
interface control document, Cooperative Institute for Research in the
Atmosphere, Colorado State University, p. 10, 2007. a
Patel, P. N., Gautam, R., Michibata, T., and Gadhavi, H.: Strengthened Indian
summer monsoon precipitation susceptibility linked to dust‐induced ice
cloud modification, Geophys. Res. Lett., 46, 8431–8441,
https://doi.org/10.1029/2018GL081634, 2019. a
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick
Hofmann, R. J.: Reconciling simulated and observed views of clouds: MODIS,
ISCCP, and the limits of instrument simulators, J. Climate, 25,
4699–4720, https://doi.org/10.1175/JCLI-D-11-00267.1, 2012. a
Polonsky, I. N.: Level 2 cloud optical depth product process description and
interface control document, Cooperative Institute for Research in the
Atmosphere, Colorado State University, p. 21, 2008. a
Quaas, J.: Approaches to Observe Anthropogenic Aerosol-Cloud Interactions,
Current Climate Change Reports, 1, 297–304, https://doi.org/10.1007/s40641-015-0028-0,
2015. a
Rosenfeld, D., Wang, H., and Rasch, P. J.: The roles of cloud drop effective
radius and LWP in determining rain properties in marine stratocumulus,
Geophys. Res. Lett., 39, L13801, https://doi.org/10.1029/2012GL052028, 2012. a
Rosenfeld, D., Zhu, Y., Minghuai, W., Zheng, Y., Goren, T., and Yu, S.:
Aerosol-driven droplet concentrations dominate converage and water of
oceanic low level clouds, Science, 363, AAV0566,
https://doi.org/10.1126/SCIENCE.AAV0566, 2019. a
Smalley, M., L'Ecuyer, T., Lebsock, M., and Haynes, J.: A Comparison of
Precipitation Occurrence from the NCEP Stage IV QPE Product and the CloudSat
Cloud Profiling Radar, J. Hydrometeorol., 15, 444–458,
https://doi.org/10.1175/JHM-D-13-048.1, 2014. a
Sorooshian, A., Feingold, G., Lebsock, M. D., Jiang, H., and Stephens, G. L.:
On the precipitation susceptibility of clouds to aerosol perturbations,
Geophys. Res. Lett., 36, L13803, https://doi.org/10.1029/2009GL038993, 2009. a
Sorooshian, A., Wang, Z., Feingold, G., and L'Ecuyer, T. S.: A satellite
perspective on cloud water to rain water conversion rates and relationships
with environmental conditions, J. Geophys. Res.-Atmos.,
118, 6643–6650, https://doi.org/10.1002/jgrd.50523, 2013. a
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L'Ecuyer,
T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten years of
actively observing the Earth system, B. Am. Meteorol.
Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018. a
Stephens, G. L. and Haynes, J. M.: Near global observations of the warm rain
coalescence process, Geophys. Res. Lett., 34, L20805,
https://doi.org/10.1029/2007GL030259, 2007. a
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z.,
Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller,
S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and Team, T. C. S.: The
Cloudsat mission and the A-Train, B. Am. Meteorol.
Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002. a
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., Durden, S., Rokey, M.,
Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J.,
Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang,
Z., and Marchand, R.: CloudSat mission: Performance and early science after
the first year of operation, J. Geophys. Res.-Atmos.,
113, D00A18, https://doi.org/10.1029/2008JD009982, 2008. a
Suzuki, K., Nakajima, T. Y., and Stephens, G. L.: Particle Growth and Drop
Collection Efficiency of Warm Clouds as Inferred from Joint CloudSat and
MODIS Observations, J. Atmos. Sci., 67, 3019–3032,
https://doi.org/10.1175/2010JAS3463.1, 2010. a, b
Suzuki, K., Stephens, G. L., van den Heever, S. C., and Nakajima, T. Y.:
Diagnosis of the Warm Rain Process in Cloud-Resolving Models Using Joint
CloudSat and MODIS Observations, J. Atmos. Sci., 68,
2655–2670, https://doi.org/10.1175/JAS-D-10-05026.1, 2011. a
Suzuki, K., Stephens, G. L., and Lebsock, M. D.: Aerosol effect on the warm
rain formation process: Satellite observations and modeling, J. Geophys. Res., 118, 170–184, https://doi.org/10.1002/jgrd.50043, 2013. a, b, c
Suzuki, K., Stephens, G., Bodas-Salcedo, A., Wang, M., Golaz, J.-C., Yokohata,
T., and Koshiro, T.: Evaluation of the Warm Rain Formation Process in Global
Models with Satellite Observations, J. Atmos. Sci., 72,
3996–4014, https://doi.org/10.1175/JAS-D-14-0265.1, 2015. a, b
Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geosci. Model Dev., 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, 2018. a, b, c, d
Szczodrak, M., Austin, P. H., and Krummel, P. B.: Variability of Optical Depth
and Effective Radius in Marine Stratocumulus Clouds, J.
Atmos. Sci., 58, 2912–2926,
https://doi.org/10.1175/1520-0469(2001)058<2912:VOODAE>2.0.CO;2, 2001. a
Takahashi, H., Lebsock, M., Suzuki, K., Stephens, G., and Wang, M.: An
investigation of microphysics and subgrid-scale variability in warm-rain
clouds using the A-Train observations and a multiscale modeling framework,
J. Geophys. Res.-Atmos., 122, 7493–7504,
https://doi.org/10.1002/2016JD026404, 2017a. a
Takahashi, H., Suzuki, K., and Stephens, G.: Land–ocean differences in the
warm-rain formation process in satellite and ground-based observations and
model simulations, Q. J. Roy. Meteorol. Soc.,
143, 1804–1815, https://doi.org/10.1002/qj.3042, 2017b. a
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. a
Terai, C. R., Wood, R., and Kubar, T. L.: Satellite estimates of precipitation
susceptibility in low-level marine stratiform clouds, J. Geophys.
Res., 120, 8878–8889, https://doi.org/10.1002/2015JD023319, 2015. a
Thayer-Calder, K., Gettelman, A., Craig, C., Goldhaber, S., Bogenschutz, P. A., Chen, C.-C., Morrison, H., Höft, J., Raut, E., Griffin, B. M., Weber, J. K., Larson, V. E., Wyant, M. C., Wang, M., Guo, Z., and Ghan, S. J.: A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model, Geosci. Model Dev., 8, 3801–3821, https://doi.org/10.5194/gmd-8-3801-2015, 2015. a
Tsushima, Y., Brient, F., Klein, S. A., Konsta, D., Nam, C. C., Qu, X., Williams, K. D., Sherwood, S. C., Suzuki, K., and Zelinka, M. D.: The Cloud Feedback Model Intercomparison Project (CFMIP) Diagnostic Codes Catalogue – metrics, diagnostics and methodologies to evaluate, understand and improve the representation of clouds and cloud feedbacks in climate models, Geosci. Model Dev., 10, 4285–4305, https://doi.org/10.5194/gmd-10-4285-2017, 2017. a, b
Turner, S., Brenguier, J.-L., and Lac, C.: A subgrid parameterization scheme for precipitation, Geosci. Model Dev., 5, 499–521, https://doi.org/10.5194/gmd-5-499-2012, 2012.
a
Wang, M., Ghan, S., Liu, X., L'Ecuyer, T. S., Zhang, K., Morrison, H.,
Ovchinnikov, M., Easter, R., Marchand, R., Chand, D., Qian, Y., and Penner,
J. E.: Constraining cloud lifetime effects of aerosols using A-Train
satellite observations, Geophys. Res. Lett., 39, L15709,
https://doi.org/10.1029/2012GL052204, 2012. a
Watanabe, M., Emori, S., Satoh, M., and Miura, H.: A PDF-based hybrid
prognostic cloud scheme for general circulation models, Clim. Dynam.,
33, 795–816, https://doi.org/10.1007/s00382-008-0489-0, 2009. a
Webb, M., Senior, C., Bony, S., and Morcrette, J.-J.: Combining ERBE and ISCCP
data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate
models, Clim. Dynam., 17, 905–922, https://doi.org/10.1007/s003820100157, 2001. a
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017. a, b
Wood, R.: Drizzle in stratiform boundary layer clouds. Part II: Microphysical
aspects., J. Atmos. Sci., 62, 3034–3050, 2005. a
Wood, R., Kubar, T. L., and Hartmann, D. L.: Understanding the importance of
microphysics and macrophysics for warm rain in marine low clouds. Part II:
Heuristic models of rain formation, J. Atmos. Sci., 66,
2973–2990, https://doi.org/10.1175/2009JAS3072.1, 2009. a
Zhang, S., Wang, M., Ghan, S. J., Ding, A., Wang, H., Zhang, K., Neubauer, D., Lohmann, U., Ferrachat, S., Takeamura, T., Gettelman, A., Morrison, H., Lee, Y., Shindell, D. T., Partridge, D. G., Stier, P., Kipling, Z., and Fu, C.: On the characteristics of aerosol indirect effect based on dynamic regimes in global climate models, Atmos. Chem. Phys., 16, 2765–2783, https://doi.org/10.5194/acp-16-2765-2016, 2016. a
Short summary
A new diagnostic tool for cloud and precipitation microphysics has been added to the latest version of the Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP2). The tool generates warm rain process statistics from several instrument simulators online during the COSP execution. This online diagnostic is intended to serve as a tool that facilitates efficient model development and the evaluation of multiple climate models.
A new diagnostic tool for cloud and precipitation microphysics has been added to the latest...
