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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A. V. Di Vittorio, L. P. Chini, B. Bond-Lamberty, J. Mao, X. Shi, J. Truesdale, A. Craig, K. Calvin, A. Jones, W. D. Collins, J. Edmonds, G. C. Hurtt, P. Thornton, and A. Thomson
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J. A. Holm, J. Q. Chambers, W. D. Collins, and N. Higuchi
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C. D. Koven, W. J. Riley, Z. M. Subin, J. Y. Tang, M. S. Torn, W. D. Collins, G. B. Bonan, D. M. Lawrence, and S. C. Swenson
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W.-C. Hsieh, W. D. Collins, Y. Liu, J. C. H. Chiang, C.-L. Shie, K. Caldeira, and L. Cao
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Atmos. Chem. Phys., 13, 3133–3147, https://doi.org/10.5194/acp-13-3133-2013, https://doi.org/10.5194/acp-13-3133-2013, 2013
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Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
The MESSy DWARF (based on MESSy v2.55.2)
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
TAMS: a tracking, classifying, and variable-assigning algorithm for mesoscale convective systems in simulated and satellite-derived datasets
Development of the adjoint of the unified tropospheric–stratospheric chemistry extension (UCX) in GEOS-Chem adjoint v36
New explicit formulae for the settling speed of prolate spheroids in the atmosphere: theoretical background and implementation in AerSett v2.0.2
ZJU-AERO V0.5: an Accurate and Efficient Radar Operator designed for CMA-GFS/MESO with the capability to simulate non-spherical hydrometeors
The Year of Polar Prediction site Model Intercomparison Project (YOPPsiteMIP) phase 1: project overview and Arctic winter forecast evaluation
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations
Global variable-resolution simulations of extreme precipitation over Henan, China, in 2021 with MPAS-Atmosphere v7.3
The CHIMERE chemistry-transport model v2023r1
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
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. 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 in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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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.
Felipe Cifuentes, Henk Eskes, Folkert Boersma, Enrico Dammers, and Charlotte Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2225, https://doi.org/10.5194/egusphere-2024-2225, 2024
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOX emissions using synthetic NO2 satellite column retrievals derived from high-resolution model simulations. The FDA accurately reproduced NOX emissions when column observations were limited to the boundary layer and when the variability of NO2 lifetime, NOX:NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces a strong model dependency, reducing the simplicity of the original FDA formulation.
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.
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...
