Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-6131-2020
© Author(s) 2020. 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-13-6131-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Dec 2020
Model description paper |
| 03 Dec 2020
| 03 Dec 2020
Detection of atmospheric rivers with inline uncertainty quantification: TECA-BARD v1.0.1
Department of Earth and Atmospheric Sciences, Indiana University, Bloomington, Indiana, USA
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Mark D. Risser
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Burlen Loring
Computational Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Abdelrahman A. Elbashandy
Computational Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Harinarayan Krishnan
Computational Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Jeffrey Johnson
Cohere Consulting LLC, Seattle, Washington, USA
Christina M. Patricola
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa, USA
John P. O'Brien
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Earth and Planetary Science, University of California, Santa Cruz, California, USA
Ankur Mahesh
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Prabhat
National Energy Research Scientific Computing Center, Lawrence Berkeley National Lab, Berkeley, California, USA
Sarahí Arriaga Ramirez
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Land, Air and Water Resources, University of California, Davis, California, USA
Alan M. Rhoades
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Alexander Charn
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Earth and Planetary Science, University of California, Berkeley, California, USA
Héctor Inda Díaz
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Land, Air and Water Resources, University of California, Davis, California, USA
William D. Collins
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Lab, Berkeley, California, USA
Department of Earth and Planetary Science, University of California, Berkeley, California, USA
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Recent atmospheric radionuclide leakages from unknown sources have posed a new challenge in nuclear emergency assessment. Reconstruction via environmental observations is the only feasible way to identify sources, but simultaneous reconstruction of the source location and release rate yields high uncertainties. We propose a spatiotemporally separated reconstruction strategy that avoids these uncertainties and outperforms state-of-the-art methods with respect to accuracy and uncertainty ranges.
Shaokun Deng, Shengmu Yang, Shengli Chen, Daoyi Chen, Xuefeng Yang, and Shanshan Cui
Geosci. Model Dev., 17, 4891–4909, https://doi.org/10.5194/gmd-17-4891-2024, https://doi.org/10.5194/gmd-17-4891-2024, 2024
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Global offshore wind power development is moving from offshore to deeper waters, where floating offshore wind turbines have an advantage over bottom-fixed turbines. However, current wind farm parameterization schemes in mesoscale models are not applicable to floating turbines. We propose a floating wind farm parameterization scheme that accounts for the attenuation of the significant wave height by floating turbines. The results indicate that it has a significant effect on the power output.
Virve Eveliina Karsisto
Geosci. Model Dev., 17, 4837–4853, https://doi.org/10.5194/gmd-17-4837-2024, https://doi.org/10.5194/gmd-17-4837-2024, 2024
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RoadSurf is an open-source library that contains functions from the Finnish Meteorological Institute’s road weather model. The evaluation of the library shows that it is well suited for making road surface temperature forecasts. The evaluation was done by making forecasts for about 400 road weather stations in Finland with the library. Accurate forecasts help road authorities perform salting and plowing operations at the right time and keep roads safe for drivers.
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
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The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
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We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
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The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
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By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
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We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
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This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
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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
Short summary
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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
Short summary
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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
Short summary
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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
Short summary
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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.
Cited articles
Chen, X., Leung, L. R., Gao, Y., Liu, Y., Wigmosta, M., and Richmond, M.:
Predictability of Extreme Precipitation in Western U.S. Watersheds Based on
Atmospheric River Occurrence, Intensity, and Duration, Geophys. Res.
Lett., 45, 11693–11701, https://doi.org/10.1029/2018GL079831, 2018. a
Chen, X., Leung, L. R., Wigmosta, M., and Richmond, M.: Impact of Atmospheric
Rivers on Surface Hydrological Processes in Western U.S. Watersheds, J. Geophys. Res.-Atmos., 124, 8896–8916,
https://doi.org/10.1029/2019JD030468, 2019. a
Dettinger, M.: Climate change, atmospheric rivers, and floods in California –
a multimodel analysis of storm frequency and magnitude changes, J.
Am. Water Resour. Assoc., 47, 514–523,
https://doi.org/10.1111/j.1752-1688.2011.00546.x, 2011. a
Dong, L., Leung, L. R., Song, F., and Lu, J.: Roles of SST versus internal
atmospheric variability in winter extreme precipitation variability along the
U.S. West Coast, J. Climate, 32, JCLI–D–18–0062.1,
https://doi.org/10.1175/JCLI-D-18-0062.1,
2018. a, b
Espinoza, V., Waliser, D. E., Guan, B., Lavers, D. A., and Ralph, F. M.:
Global Analysis of Climate Change Projection Effects on Atmospheric Rivers,
Geophys. Res. Lett., 45, 4299–4308, https://doi.org/10.1029/2017GL076968,
2018. a
Foreman-Mackey, D., Hogg, D. W., Lang, D., and Goodman, J.: emcee : The MCMC
Hammer, Publ. Astron. Soc. Pac., 125,
306–312, https://doi.org/10.1086/670067, 2013. a, b, c
Gao, Y., Lu, J., Leung, L. R., Yang, Q., Hagos, S., and Qian, Y.: Dynamical
and thermodynamical modulations on future changes of landfalling atmospheric
rivers over western North America, Geophys. Res. Lett., 42,
7179–7186, https://doi.org/10.1002/2015GL065435, 2015. a, b
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G. K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis
for research and applications, version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gelman, A.: Prior distributions for variance parameters in hierarchical models
(comment on article by Browne and Draper), Bayesian Analysis, 1, 515–534,
https://doi.org/10.1214/06-BA117A, 2006. a
Gershunov, A., Shulgina, T., Ralph, F. M., Lavers, D. A., and Rutz, J. J.:
Assessing the climate-scale variability of atmospheric rivers affecting
western North America, Geophys. Res. Lett., 44, 7900–7908,
https://doi.org/10.1002/2017GL074175, 2017. a, b
Gershunov, A., Shulgina, T., Clemesha, R. E. S., Guirguis, K., Pierce, D. W.,
Dettinger, M. D., Lavers, D. A., Cayan, D. R., Polade, S. D., Kalansky, J.,
and Ralph, F. M.: Precipitation regime change in Western North America: The
role of Atmospheric Rivers, Sci. Rep.-UK, 9, 9944,
https://doi.org/10.1038/s41598-019-46169-w,
2019a. a, b
Gershunov, A., Shulgina, T., Clemesha, R. E. S., Guirguis, K., Pierce, D. W.,
Dettinger, M. D., Lavers, D. A., Cayan, D. R., Polade, S. D., Kalansky, J.,
and Ralph, F. M.: Precipitation regime change in Western North America: The
role of Atmospheric Rivers, Sci. Rep.-UK, 9, 9944,
https://doi.org/10.1038/s41598-019-46169-w, 2019b. a
Goldenson, N., Leung, L. R., Bitz, C. M., and Blanchard-Wrigglesworth, E.:
Influence of Atmospheric Rivers on Mountain Snowpack in the Western United
States, J. Climate, 31, 9921–9940, https://doi.org/10.1175/JCLI-D-18-0268.1,
2018. a, b
Goodman, J. and Weare, J.: Ensemble samplers with affine invariance,
Comm. App. Math. Com. Sc., 5, 65–80,
https://doi.org/10.2140/camcos.2010.5.65, 2010. a, b
Gorodetskaya, I. V., Tsukernik, M., Claes, K., Ralph, M. F., Neff, W. D., and
Van Lipzig, N. P. M.: The role of atmospheric rivers in anomalous snow
accumulation in East Antarctica, Geophys. Res. Lett., 41,
6199–6206, https://doi.org/10.1002/2014GL060881, 2014. a
Guan, B. and Waliser, D. E.: Detection of atmospheric rivers: Evaluation and
application of an algorithm for global studies, J. Geophys.
Res.-Atmos., 120, 12514–12535, https://doi.org/10.1002/2015JD024257,
2015a. a, b
Guan, B. and Waliser, D. E.: Detection of atmospheric rivers: Evaluation and
application of an algorithm for global studies, J. Geophys.
Res.-Atmos., 120, 12514–12535, https://doi.org/10.1002/2015JD024257,
2015b. a, b, c
Hu, H., Dominguez, F., Wang, Z., Lavers, D. A., Zhang, G., and Ralph, F. M.:
Linking Atmospheric River Hydrological Impacts on the U.S. West Coast to
Rossby Wave Breaking, J. Climate, 30, 3381–3399,
https://doi.org/10.1175/JCLI-D-16-0386.1, 2017. a
Huning, L. S., Margulis, S. A., Guan, B., Waliser, D. E., and Neiman, P. J.:
Implications of Detection Methods on Characterizing Atmospheric River
Contribution to Seasonal Snowfall Across Sierra Nevada, USA, Geophys. Res. Lett., 44, 10,445–10,453, https://doi.org/10.1002/2017GL075201, 2017. a, b
Huning, L. S., Guan, B., Waliser, D. E., and Lettenmaier, D. P.: Sensitivity
of Seasonal Snowfall Attribution to Atmospheric Rivers and Their
Reanalysis-Based Detection, Geophys. Res. Lett., 46, 794–803,
https://doi.org/10.1029/2018GL080783, 2019. a
Kunkel, K. E., Easterling, D. R., Kristovich, D. A. R., Gleason, B., Stoecker,
L., and Smith, R.: Meteorological Causes of the Secular Variations in
Observed Extreme Precipitation Events for the Conterminous United States,
J. Hydrometeorol., 13, 1131–1141, https://doi.org/10.1175/JHM-D-11-0108.1,
2012. a
Leung, L.-R. and Qian, Y.: Atmospheric rivers induced heavy precipitation
and flooding in the western U.S. simulated by the WRF regional climate
model, Geophys. Res. Lett., 36, 1–6, https://doi.org/10.1029/2008GL036445,
2009. a
Loring, B., O'Brien, T. A., Elbashandy, A. E., Johnson, J. N., Krishnan, H.,
Keen, N., Prabhat, and SFA, C.: Toolkit for Extreme Climate Analysis – TECA
Bayesian AR Detector v1.0.1, Zenodo, https://doi.org/10.5281/zenodo.4130468, 2020. a, b
Neiman, P. J., Ralph, F. M., Wick, G. A., Lundquist, J. D., and Dettinger,
M. D.: Meteorological characteristics and overland precipitation impacts of
atmospheric rivers affecting the West coast of North America based on eight
years of SSM/I satellite observations, J. Hydrometeorol., 9,
22–47, https://doi.org/10.1175/2007JHM855.1, 2008. a
Neu, U., Akperov, M., Bellenbaum, N., Benestad, R., Blender, R., Caballero, R.,
Cocozza, A., Dacre, H., Feng, Y., Fraedrich, K., Grieger, J., Gulev, S.,
Hanley, J., Hewson, T., Inatsu, M., Keay, K., Kew, S., Kindem, I.,
Leckebusch, G. C., Liberato, M., Lionello, P., Mokhov, I., Pinto, J., Raible,
C., Reale, M., Rudeva, I., Schuster, M., Simmonds, I., Sinclair, M.,
Sprenger, M., Tilinina, N., Trigo, I., Ulbrich, S., Ulbrich, U., Wang, X.,
and Wernli, H.: IMILAST: A Community Effort to Intercompare Extratropical
Cyclone Detection and Tracking Algorithms, B. Am.
Meteorol. Soc., 94, 529–547, https://doi.org/10.1175/BAMS-D-11-00154.1, 2013. a, b
O'Brien, T. A.: TECA Bayesian AR Detector Training, Zenodo,
https://doi.org/10.5281/zenodo.4130486, 2020. a
O'Brien, T. A., Kashinath, K., Cavanaugh, N. R., Collins, W. D., and O'Brien,
J. P.: A fast and objective multidimensional kernel density estimation
method: FastKDE, Computational Statistics and Data Analysis, 101, 148–160,
https://doi.org/10.1016/j.csda.2016.02.014,
2016. a
O'Brien, T. A., Patricola, C. M., O'Brien, J. P., Mahesh, A., Arriaga-Ramirez,
S., Rhoades, A. M., Charn, A., and Inda-Diaz, H.: Expert AR Detector Counts, Zenodo,
https://doi.org/10.5281/zenodo.4130559, 2020a. a
O'Brien, T. A., Payne, A. E., Shields, C. A., Rutz, J., Brands, S., Castellano,
C., Chen, J., Cleveland, W., DeFlorio, M. J., Goldenson, N., Gorodetskaya,
I. V., Díaz, H. I., Kashinath, K., Kawzenuk, B., Kim, S., Krinitskiy,
M., Lora, J. M., McClenny, B., Michaelis, A., O'Brien, J. P., Patricola,
C. M., Ramos, A. M., Shearer, E. J., Tung, W.-w., Ullrich, P. A., Wehner,
M. F., Yang, K., Zhang, R., Zhang, Z., and Zhou, Y.: Detection Uncertainty
Matters for Understanding Atmospheric Rivers, B. Am.
Meteorol. Soc., 101, E790–E796, https://doi.org/10.1175/BAMS-D-19-0348.1,
2020b. a
Patricola, C. M., O'Brien, J. P., Risser, M. D., Rhoades, A. M., O'Brien, T. A., Ullrich, P. A., Stone, D. A., and Collins, W. D.: Maximizing ENSO as a source of western US hydroclimate predictability, Clim. Dynam., 54, 351–372, https://doi.org/10.1007/s00382-019-05004-8, 2020. a
Payne, A. E. and Magnusdottir, G.: An evaluation of atmospheric rivers over
the North Pacific in CMIP5 and their response to warming under RCP 8.5,
J. Geophys. Res., 120, 11173–11190,
https://doi.org/10.1002/2015JD023586, 2015. a
Payne, A. E., Demory, M.-E., Leung, L. R., Ramos, A. M., Shields, C. A., Rutz,
J. J., Siler, N., Villarini, G., Hall, A., and Ralph, F. M.: Responses and
impacts of atmospheric rivers to climate change, Nat. Rev. Earth
Environ., 1, 143–157, https://doi.org/10.1038/s43017-020-0030-5, 2020. a, b, c
Prabhat, Kashinath, K., Mudigonda, M., Kim, S., Kapp-Schwoerer, L., Graubner, A., Karaismailoglu, E., von Kleist, L., Kurth, T., Greiner, A., Yang, K., Lewis, C., Chen, J., Lou, A., Chandran, S., Toms, B., Chapman, W., Dagon, K., Shields, C. A., O'Brien, T., Wehner, M., and Collins, W.: ClimateNet: an expert-labelled open dataset and Deep Learning architecture for enabling high-precision analyses of extreme weather, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-72, in review, 2020. a, b, c
Ralph, F. M., Coleman, T., Neiman, P. J., Zamora, R. J., and Dettinger, M. D.:
Observed Impacts of Duration and Seasonality of Atmospheric-River Landfalls
on Soil Moisture and Runoff in Coastal Northern California, J.
Hydrometeorol., 14, 443–459, https://doi.org/10.1175/JHM-D-12-076.1,
2013. a
Ralph, F. M., Dettinger, M. D., Cairns, M. M., Galarneau, T. J., and Eylander,
J.: Defining “Atmospheric River”: How the Glossary of Meteorology Helped
Resolve a Debate, B. Am. Meteorol. Soc., 99,
837–839, https://doi.org/10.1175/BAMS-D-17-0157.1, 2018. a
Ralph, F. M., Rutz, J. J., Cordeira, J. M., Dettinger, M., Anderson, M.,
Reynolds, D., Schick, L. J., and Smallcomb, C.: A Scale to Characterize the
Strength and Impacts of Atmospheric Rivers, B. Am.
Meteorol. Soc., 100, 269–289, https://doi.org/10.1175/BAMS-D-18-0023.1,
2019a. a
Ralph, F. M., Wilson, A. M., Shulgina, T., Kawzenuk, B., Sellars, S., Rutz,
J. J., Lamjiri, M. A., Barnes, E. A., Gershunov, A., Guan, B., Nardi, K. M.,
Osborne, T., and Wick, G. A.: ARTMIP-early start comparison of atmospheric
river detection tools: how many atmospheric rivers hit northern California's
Russian River watershed?, Clim. Dynam., 52, 4973–4994,
https://doi.org/10.1007/s00382-018-4427-5, 2019b. a, b, c, d, e
Rutz, J. J., Steenburgh, W. J., and Ralph, F. M.: Climatological
Characteristics of Atmospheric Rivers and Their Inland Penetration over the
Western United States, Mon. Weather Rev., 142, 905–921,
https://doi.org/10.1175/MWR-D-13-00168.1,
2014. a, b
Rutz, J. J., Shields, C. A., Lora, J. M., Payne, A. E., Guan, B., Ullrich, P.,
O'Brien, T., Leung, L. R., Ralph, F. M., Wehner, M., Brands, S., Collow, A.,
Goldenson, N., Gorodetskaya, I., Griffith, H., Kashinath, K., Kawzenuk, B.,
Krishnan, H., Kurlin, V., Lavers, D., Magnusdottir, G., Mahoney, K.,
McClenny, E., Muszynski, G., Nguyen, P. D., Prabhat, Qian, Y., Ramos, A. M.,
Sarangi, C., Sellars, S., Shulgina, T., Tome, R., Waliser, D., Walton, D.,
Wick, G., Wilson, A. M., and Viale, M.: The Atmospheric River Tracking
Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in
Atmospheric River Climatology, J. Geophys. Res.-Atmos.,
53, 2019JD030936, https://doi.org/10.1029/2019JD030936,
2019. a, b, c
Schemm, S., Sprenger, M., and Wernli, H.: When during Their Life Cycle Are
Extratropical Cyclones Attended by Fronts?, B. Am.
Meteorol. Soc., 99, 149–165, https://doi.org/10.1175/BAMS-D-16-0261.1,
2018. a, b
Sellars, S. L., Kawzenuk, B., Nguyen, P., Ralph, F. M., and Sorooshian, S.:
Genesis, Pathways, and Terminations of Intense Global Water Vapor Transport
in Association with Large-Scale Climate Patterns, Geophys. Res. Lett., 44, 12465–12475, https://doi.org/10.1002/2017GL075495, 2017. a
Shields, C. A. and Kiehl, J. T.: Simulating the Pineapple Express in the half
degree Community Climate System Model, CCSM4, Geophys. Res. Lett.,
43, 7767–7773, https://doi.org/10.1002/2016GL069476, 2016. a
Shields, C. A., Rutz, J. J., Leung, L.-Y., Ralph, F. M., Wehner, M., Kawzenuk, B., Lora, J. M., McClenny, E., Osborne, T., Payne, A. E., Ullrich, P., Gershunov, A., Goldenson, N., Guan, B., Qian, Y., Ramos, A. M., Sarangi, C., Sellars, S., Gorodetskaya, I., Kashinath, K., Kurlin, V., Mahoney, K., Muszynski, G., Pierce, R., Subramanian, A. C., Tome, R., Waliser, D., Walton, D., Wick, G., Wilson, A., Lavers, D., Prabhat, Collow, A., Krishnan, H., Magnusdottir, G., and Nguyen, P.: Atmospheric River Tracking Method Intercomparison Project (ARTMIP): project goals and experimental design, Geosci. Model Dev., 11, 2455–2474, https://doi.org/10.5194/gmd-11-2455-2018, 2018. a, b, c, d, e
Shields, C. A., Rosenbloom, N., Bates, S., Hannay, C., Hu, A., Payne, A. E.,
Rutz, J. J., and Truesdale, J.: Meridional Heat Transport During Atmospheric
Rivers in High-Resolution CESM Climate Projections, Geophys. Res. Lett., 46, 14702–14712, https://doi.org/10.1029/2019GL085565,
2019a. a
Shields, C. A., Rutz, J. J., Leung, L. R., Ralph, F. M., Wehner, M., O'Brien,
T., and Pierce, R.: Defining Uncertainties through Comparison of Atmospheric
River Tracking Methods, Bulletin of the American Meteorological Society,
100, ES93–ES96, https://doi.org/10.1175/BAMS-D-18-0200.1,
2019b. a
Vishnu, S., Boos, W. R., Ullrich, P. A., and O'Brien, T. A.: Assessing Historical Variability of South Asian Monsoon Lows and Depressions With an Optimized Tracking Algorithm, J. Geophys. Res.-Atmos., 125, e2020JD032977, https://doi.org/10.1029/2020JD032977, 2020. a, b
Waliser, D. E., Moncrieff, M. W., Burridge, D., Fink, A. H., Gochis, D.,
Goswami, B. N., Guan, B., Harr, P., Heming, J., Hsu, H.-H., Jakob, C.,
Janiga, M., Johnson, R., Jones, S., Knippertz, P., Marengo, J., Nguyen, H.,
Pope, M., Serra, Y., Thorncroft, C., Wheeler, M., Wood, R., and Yuter, S.:
The “Year” of Tropical Convection (May 2008–April 2010): Climate
Variability and Weather Highlights, B. Am. Meteorol.
Soc., 93, 1189–1218, https://doi.org/10.1175/2011BAMS3095.1,
2012.
a
Walsh, K., Camargo, S., Vecchi, G., Daloz, A., Elsner, J., Emanuel, K., Horn,
M., Lim, Y.-K., Roberts, M., Patricola, C., Scoccimarro, E., Sobel, A.,
Strazzo, S., Villarini, G., Wehner, M., Zhao, M., Kossin, J., LaRow, T.,
Oouchi, K., Schubert, S., Wang, H., Bacmeister, J., Chang, P., Chauvin, F.,
Jablonowski, C., Kumar, A., Murakami, H., Ose, T., Reed, K., Saravanan, R.,
Yamada, Y., Zarzycki, C., Vidale, P., Jonas, J., and Henderson, N.:
Hurricanes and Climate: The U.S. CLIVAR Working Group on Hurricanes,
B. Am. Meteorol. Soc., 96, 997–1017,
https://doi.org/10.1175/BAMS-D-13-00242.1, 2015. a, b
Warner, M. D., Mass, C. F., and Salathé, E. P.: Changes in Winter
Atmospheric Rivers along the North American West Coast in CMIP5 Climate
Models, J. Hydrometeorol., 16, 118–128,
https://doi.org/10.1175/JHM-D-14-0080.1,
2015. a
Wehner, M. F., Reed, K. A., Loring, B., Stone, D., and Krishnan, H.: Changes in tropical cyclones under stabilized 1.5 and 2.0 ∘C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols, Earth Syst. Dynam., 9, 187–195, https://doi.org/10.5194/esd-9-187-2018, 2018. a
Williams, I. N. and Patricola, C. M.: Diversity of ENSO Events Unified by
Convective Threshold Sea Surface Temperature: A Nonlinear ENSO Index,
Geophys. Res. Lett., 45, 9236–9244, https://doi.org/10.1029/2018GL079203, 2018. a
Zarzycki, C. M. and Ullrich, P. A.: Assessing sensitivities in algorithmic
detection of tropical cyclones in climate data, Geophys. Res. Lett., 44, 1141–1149, https://doi.org/10.1002/2016GL071606, 2017. a, b
Zarzycki, C. M., Thatcher, D. R., and Jablonowski, C.: Objective tropical
cyclone extratropical transition detection in high-resolution reanalysis and
climate model data, J. Adv. Model. Earth Syst., 9,
130–148, https://doi.org/10.1002/2016MS000775, 2017. a
Zhou, Y., O'Brien, T. A., Ullrich, P. A., Collins, W. D., Patricola, C. M., and Rhoades, A. M.: Uncertainties in Atmospheric River Life Cycles by Detection Algorithms: Climatology and Variability, J. Geophys. Res.-Atmos., in review, https://doi.org/10.1002/essoar.10504174.1, 2020. a
Zhu, Y. and Newell, R. E.: A Proposed Algorithm for Moisture Fluxes from
Atmospheric Rivers, Mon. Weather Rev., 126, 725–735,
https://doi.org/10.1175/1520-0493(1998)126<0725:APAFMF>2.0.CO;2,
1998. a, b, c, d
Short summary
Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer this question: given a
plausibleweather event detector, how does uncertainty in the detector impact scientific results? We generate a suite of statistical models that emulate expert identification of weather features. We find that the connection between El Niño and atmospheric rivers – a specific extreme weather type – depends systematically on the design of the detector.
Researchers utilize various algorithms to identify extreme weather features in climate data, and...
