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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M. D. Lebsock, K. Suzuki, L. F. Millán, and P. M. Kalmus
Atmos. Meas. Tech., 8, 3631–3645, https://doi.org/10.5194/amt-8-3631-2015, https://doi.org/10.5194/amt-8-3631-2015, 2015
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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
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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.
M. Abe, T. Nozawa, T. Ogura, and K. Takata
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-15-17527-2015, https://doi.org/10.5194/acpd-15-17527-2015, 2015
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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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
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Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
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Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
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We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
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Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
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RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
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We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
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Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
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Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
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AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
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Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-129, https://doi.org/10.5194/gmd-2024-129, 2024
Revised manuscript accepted for GMD
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Orbital-radar is a Python tool transferring sub-orbital radar data (ground-based, airborne, and forward-simulated NWP) into synthetical space-borne cloud profiling radar data mimicking the platform characteristics, e.g. EarthCARE or CloudSat CPR. The novelty of orbital-radar is the simulation platform characteristic noise floors and errors. By this long time data sets can be transformed into synthetic observations for Cal/Valor sensitivity studies for new or future satellite missions.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
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The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
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In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
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A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
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The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
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Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
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This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
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Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
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This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
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This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
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Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Kelly M. Núñez Ocasio and Zachary L. Moon
Geosci. Model Dev., 17, 6035–6049, https://doi.org/10.5194/gmd-17-6035-2024, https://doi.org/10.5194/gmd-17-6035-2024, 2024
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TAMS is an open-source Python-based package for tracking and classifying mesoscale convective systems 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.
Irene C. Dedoussi, Daven K. Henze, Sebastian D. Eastham, Raymond L. Speth, and Steven R. H. Barrett
Geosci. Model Dev., 17, 5689–5703, https://doi.org/10.5194/gmd-17-5689-2024, https://doi.org/10.5194/gmd-17-5689-2024, 2024
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Atmospheric model gradients provide a meaningful tool for better understanding the underlying atmospheric processes. Adjoint modeling enables computationally efficient gradient calculations. We present the adjoint of the GEOS-Chem unified chemistry extension (UCX). With this development, the GEOS-Chem adjoint model can capture stratospheric ozone and other processes jointly with tropospheric processes. We apply it to characterize the Antarctic ozone depletion potential of active halogen species.
Sylvain Mailler, Sotirios Mallios, Arineh Cholakian, Vassilis Amiridis, Laurent Menut, and Romain Pennel
Geosci. Model Dev., 17, 5641–5655, https://doi.org/10.5194/gmd-17-5641-2024, https://doi.org/10.5194/gmd-17-5641-2024, 2024
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We propose two explicit expressions to calculate the settling speed of solid atmospheric particles with prolate spheroidal shapes. The first formulation is based on theoretical arguments only, while the second one is based on computational fluid dynamics calculations. We show that the first method is suitable for virtually all atmospheric aerosols, provided their shape can be adequately described as a prolate spheroid, and we provide an implementation of the first method in AerSett v2.0.2.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
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A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jonathan J. Day, Gunilla Svensson, Barbara Casati, Taneil Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie M. Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
Geosci. Model Dev., 17, 5511–5543, https://doi.org/10.5194/gmd-17-5511-2024, https://doi.org/10.5194/gmd-17-5511-2024, 2024
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The YOPP site Model Intercomparison Project (YOPPsiteMIP), which was designed to facilitate enhanced weather forecast evaluation in polar regions, is discussed here, focussing on describing the archive of forecast data and presenting a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance that is common across models and a related inability to forecast cold extremes at several of the sites.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
Geosci. Model Dev., 17, 5545–5571, https://doi.org/10.5194/gmd-17-5545-2024, https://doi.org/10.5194/gmd-17-5545-2024, 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 in 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's capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Zijun Liu, Li Dong, Zongxu Qiu, Xingrong Li, Huiling Yuan, Dongmei Meng, Xiaobin Qiu, Dingyuan Liang, and Yafei Wang
Geosci. Model Dev., 17, 5477–5496, https://doi.org/10.5194/gmd-17-5477-2024, https://doi.org/10.5194/gmd-17-5477-2024, 2024
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In this study, we completed a series of simulations with MPAS-Atmosphere (version 7.3) to study the extreme precipitation event of Henan, China, during 20–22 July 2021. We found the different performance of two built-in parameterization scheme suites (mesoscale and convection-permitting suites) with global quasi-uniform and variable-resolution meshes. This study holds significant implications for advancing the understanding of the scale-aware capability of MPAS-Atmosphere.
Laurent Menut, Arineh Cholakian, Romain Pennel, Guillaume Siour, Sylvain Mailler, Myrto Valari, Lya Lugon, and Yann Meurdesoif
Geosci. Model Dev., 17, 5431–5457, https://doi.org/10.5194/gmd-17-5431-2024, https://doi.org/10.5194/gmd-17-5431-2024, 2024
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A new version of the CHIMERE model is presented. This version contains both computational and physico-chemical changes. The computational changes make it easy to choose the variables to be extracted as a result, including values of maximum sub-hourly concentrations. Performance tests show that the model is 1.5 to 2 times faster than the previous version for the same setup. Processes such as turbulence, transport schemes and dry deposition have been modified and updated.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
Geosci. Model Dev., 17, 5225–5247, https://doi.org/10.5194/gmd-17-5225-2024, https://doi.org/10.5194/gmd-17-5225-2024, 2024
Short summary
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A Merged Observatory Data File (MODF) format to systematically collate complex atmosphere, ocean, and terrestrial data sets collected by multiple instruments during field campaigns is presented. The MODF format is also designed to be applied to model output data, yielding format-matching Merged Model Data Files (MMDFs). MODFs plus MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
Short summary
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1437, https://doi.org/10.5194/egusphere-2024-1437, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can well reproduce the distribution of PAHs. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change of BaP is less than PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although "the Action Plan" has been implemented.
Alberto Martilli, Negin Nazarian, E. Scott Krayenhoff, Jacob Lachapelle, Jiachen Lu, Esther Rivas, Alejandro Rodriguez-Sanchez, Beatriz Sanchez, and José Luis Santiago
Geosci. Model Dev., 17, 5023–5039, https://doi.org/10.5194/gmd-17-5023-2024, https://doi.org/10.5194/gmd-17-5023-2024, 2024
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Here, we present a model that quantifies the thermal stress and its microscale variability at a city scale with a mesoscale model. This tool can have multiple applications, from early warnings of extreme heat to the vulnerable population to the evaluation of the effectiveness of heat mitigation strategies. It is the first model that includes information on microscale variability in a mesoscale model, something that is essential for fully evaluating heat stress.
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...
