Articles | Volume 5, issue 3
https://doi.org/10.5194/wcd-5-1117-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-5-1117-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Sep 2024
Research article |
| 23 Sep 2024
| 23 Sep 2024
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
Annelise Waling
CORRESPONDING AUTHOR
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Adam Herrington
National Center for Atmospheric Research, Boulder, CO, USA
Katharine Duderstadt
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Jack Dibb
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Elizabeth Burakowski
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Related authors
No articles found.
Laura M. D. Heinlein, Junwei He, Michael Oluwatoyin Sunday, Fangzhou Guo, James Campbell, Allison Moon, Sukriti Kapur, Ting Fang, Kasey Edwards, Meeta Cesler-Maloney, Alyssa J. Burns, Jack Dibb, William Simpson, Manabu Shiraiwa, Becky Alexander, Jingqiu Mao, James H. Flynn III, Jochen Stutz, and Cort Anastasio
Atmos. Chem. Phys., 25, 9561–9581, https://doi.org/10.5194/acp-25-9561-2025, https://doi.org/10.5194/acp-25-9561-2025, 2025
Short summary
Short summary
High-latitude cities like Fairbanks, Alaska, experience severe wintertime pollution episodes. While conventional wisdom holds that oxidation is slow under these conditions, field measurements find oxidized products in particles. To explore this, we measured oxidants in aqueous extracts of winter particles from Fairbanks. We find high concentrations of oxidants during illumination experiments, indicating that particle photochemistry can be significant even in high latitudes during winter.
Michael Oluwatoyin Sunday, Laura Marie Dahler Heinlein, Junwei He, Allison Moon, Sukriti Kapur, Ting Fang, Kasey C. Edwards, Fangzhou Guo, Jack Dibb, James H. Flynn III, Becky Alexander, Manabu Shiraiwa, and Cort Anastasio
Atmos. Chem. Phys., 25, 5087–5100, https://doi.org/10.5194/acp-25-5087-2025, https://doi.org/10.5194/acp-25-5087-2025, 2025
Short summary
Short summary
Hydrogen peroxide (HOOH) is an important oxidant that forms atmospheric sulfate. We demonstrate that the illumination of brown carbon can rapidly form HOOH within particles, even under the low-sunlight conditions of Fairbanks, Alaska, during winter. This in-particle formation of HOOH is fast enough that it forms sulfate at significant rates. In contrast, the formation of HOOH in the gas phase during the campaign is expected to be negligible because of high NOx levels.
René R. Wijngaard, Willem Jan van de Berg, Christaan T. van Dalum, Adam R. Herrington, and Xavier J. Levine
EGUsphere, https://doi.org/10.5194/egusphere-2025-1070, https://doi.org/10.5194/egusphere-2025-1070, 2025
Short summary
Short summary
We used the variable-resolution CESM to simulate present-day and future temperature and precipitation extremes in the Arctic by applying global grids (~111 km) with and without regional refinement (~28 km) and following a storyline approach. We found that global grids with (without) regional refinement generally perform better in simulating present-day precipitation (temperature) extremes, and that future high (low) temperature and wet precipitation extremes are projected to increase (decrease).
Jan-Lukas Tirpitz, Santo Fedele Colosimo, Nathaniel Brockway, Robert Spurr, Matt Christi, Samuel Hall, Kirk Ullmann, Johnathan Hair, Taylor Shingler, Rodney Weber, Jack Dibb, Richard Moore, Elizabeth Wiggins, Vijay Natraj, Nicolas Theys, and Jochen Stutz
Atmos. Chem. Phys., 25, 1989–2015, https://doi.org/10.5194/acp-25-1989-2025, https://doi.org/10.5194/acp-25-1989-2025, 2025
Short summary
Short summary
We combine plume composition data from the 2019 NASA FIREX-AQ campaign with state-of-the-art radiative transfer modeling techniques to calculate distributions of actinic flux and photolysis frequencies in a wildfire plume. Excellent agreement of the model and observations demonstrates the applicability of this approach to constrain photochemistry in such plumes. We identify limiting factors for the modeling accuracy and discuss spatial and spectral features of the distributions.
Kouji Adachi, Jack E. Dibb, Joseph M. Katich, Joshua P. Schwarz, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Jeff Peischl, Christopher D. Holmes, and James Crawford
Atmos. Chem. Phys., 24, 10985–11004, https://doi.org/10.5194/acp-24-10985-2024, https://doi.org/10.5194/acp-24-10985-2024, 2024
Short summary
Short summary
We examined aerosol particles from wildfires and identified tarballs (TBs) from the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign. This study reveals the compositions, abundance, sizes, and mixing states of TBs and shows that TBs formed as the smoke aged for up to 5 h. This study provides measurements of TBs from various biomass-burning events and ages, enhancing our knowledge of TB emissions and our understanding of their climate impact.
Rajashree Tri Datta, Adam Herrington, Jan T. M. Lenaerts, David P. Schneider, Luke Trusel, Ziqi Yin, and Devon Dunmire
The Cryosphere, 17, 3847–3866, https://doi.org/10.5194/tc-17-3847-2023, https://doi.org/10.5194/tc-17-3847-2023, 2023
Short summary
Short summary
Precipitation over Antarctica is one of the greatest sources of uncertainty in sea level rise estimates. Earth system models (ESMs) are a valuable tool for these estimates but typically run at coarse spatial resolutions. Here, we present an evaluation of the variable-resolution CESM2 (VR-CESM2) for the first time with a grid designed for enhanced spatial resolution over Antarctica to achieve the high resolution of regional climate models while preserving the two-way interactions of ESMs.
René R. Wijngaard, Adam R. Herrington, William H. Lipscomb, Gunter R. Leguy, and Soon-Il An
The Cryosphere, 17, 3803–3828, https://doi.org/10.5194/tc-17-3803-2023, https://doi.org/10.5194/tc-17-3803-2023, 2023
Short summary
Short summary
We evaluate the ability of the Community Earth System Model (CESM2) to simulate cryospheric–hydrological variables, such as glacier surface mass balance (SMB), over High Mountain Asia (HMA) by using a global grid (~111 km) with regional refinement (~7 km) over HMA. Evaluations of two different simulations show that climatological biases are reduced, and glacier SMB is improved (but still too negative) by modifying the snow and glacier model and using an updated glacier cover dataset.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere, 17, 3435–3442, https://doi.org/10.5194/tc-17-3435-2023, https://doi.org/10.5194/tc-17-3435-2023, 2023
Short summary
Short summary
This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
Haihui Zhu, Randall V. Martin, Betty Croft, Shixian Zhai, Chi Li, Liam Bindle, Jeffrey R. Pierce, Rachel Y.-W. Chang, Bruce E. Anderson, Luke D. Ziemba, Johnathan W. Hair, Richard A. Ferrare, Chris A. Hostetler, Inderjeet Singh, Deepangsu Chatterjee, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jack E. Dibb, Joshua S. Schwarz, and Andrew Weinheimer
Atmos. Chem. Phys., 23, 5023–5042, https://doi.org/10.5194/acp-23-5023-2023, https://doi.org/10.5194/acp-23-5023-2023, 2023
Short summary
Short summary
Particle size of atmospheric aerosol is important for estimating its climate and health effects, but simulating atmospheric aerosol size is computationally demanding. This study derives a simple parameterization of the size of organic and secondary inorganic ambient aerosol that can be applied to atmospheric models. Applying this parameterization allows a better representation of the global spatial pattern of aerosol size, as verified by ground and airborne measurements.
Shixian Zhai, Daniel J. Jacob, Drew C. Pendergrass, Nadia K. Colombi, Viral Shah, Laura Hyesung Yang, Qiang Zhang, Shuxiao Wang, Hwajin Kim, Yele Sun, Jin-Soo Choi, Jin-Soo Park, Gan Luo, Fangqun Yu, Jung-Hun Woo, Younha Kim, Jack E. Dibb, Taehyoung Lee, Jin-Seok Han, Bruce E. Anderson, Ke Li, and Hong Liao
Atmos. Chem. Phys., 23, 4271–4281, https://doi.org/10.5194/acp-23-4271-2023, https://doi.org/10.5194/acp-23-4271-2023, 2023
Short summary
Short summary
Anthropogenic fugitive dust in East Asia not only causes severe coarse particulate matter air pollution problems, but also affects fine particulate nitrate. Due to emission control efforts, coarse PM decreased steadily. We find that the decrease of coarse PM is a major driver for a lack of decrease of fine particulate nitrate, as it allows more nitric acid to form fine particulate nitrate. The continuing decrease of coarse PM requires more stringent ammonia and nitrogen oxides emission controls.
Pamela S. Rickly, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Glenn M. Wolfe, Ryan Bennett, Ilann Bourgeois, John D. Crounse, Jack E. Dibb, Joshua P. DiGangi, Glenn S. Diskin, Maximilian Dollner, Emily M. Gargulinski, Samuel R. Hall, Hannah S. Halliday, Thomas F. Hanisco, Reem A. Hannun, Jin Liao, Richard Moore, Benjamin A. Nault, John B. Nowak, Jeff Peischl, Claire E. Robinson, Thomas Ryerson, Kevin J. Sanchez, Manuel Schöberl, Amber J. Soja, Jason M. St. Clair, Kenneth L. Thornhill, Kirk Ullmann, Paul O. Wennberg, Bernadett Weinzierl, Elizabeth B. Wiggins, Edward L. Winstead, and Andrew W. Rollins
Atmos. Chem. Phys., 22, 15603–15620, https://doi.org/10.5194/acp-22-15603-2022, https://doi.org/10.5194/acp-22-15603-2022, 2022
Short summary
Short summary
Biomass burning sulfur dioxide (SO2) emission factors range from 0.27–1.1 g kg-1 C. Biomass burning SO2 can quickly form sulfate and organosulfur, but these pathways are dependent on liquid water content and pH. Hydroxymethanesulfonate (HMS) appears to be directly emitted from some fire sources but is not the sole contributor to the organosulfur signal. It is shown that HMS and organosulfur chemistry may be an important S(IV) reservoir with the fate dependent on the surrounding conditions.
Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, and Michael Duda
Geosci. Model Dev., 15, 8135–8151, https://doi.org/10.5194/gmd-15-8135-2022, https://doi.org/10.5194/gmd-15-8135-2022, 2022
Short summary
Short summary
We focus on the recent development of a state-of-the-art storm-resolving global climate model and investigate how this next-generation model performs for precipitation prediction over the western USA. Results show realistic representations of precipitation with significantly enhanced snowpack over complex terrains. The model evaluation advances the unified modeling of large-scale forcing constraints and realistic fine-scale features to advance multi-scale climate predictions and changes.
Youhua Tang, Patrick C. Campbell, Pius Lee, Rick Saylor, Fanglin Yang, Barry Baker, Daniel Tong, Ariel Stein, Jianping Huang, Ho-Chun Huang, Li Pan, Jeff McQueen, Ivanka Stajner, Jose Tirado-Delgado, Youngsun Jung, Melissa Yang, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Donald Blake, Joshua Schwarz, Jose-Luis Jimenez, James Crawford, Glenn Diskin, Richard Moore, Johnathan Hair, Greg Huey, Andrew Rollins, Jack Dibb, and Xiaoyang Zhang
Geosci. Model Dev., 15, 7977–7999, https://doi.org/10.5194/gmd-15-7977-2022, https://doi.org/10.5194/gmd-15-7977-2022, 2022
Short summary
Short summary
This paper compares two meteorological datasets for driving a regional air quality model: a regional meteorological model using WRF (WRF-CMAQ) and direct interpolation from an operational global model (GFS-CMAQ). In the comparison with surface measurements and aircraft data in summer 2019, these two methods show mixed performance depending on the corresponding meteorological settings. Direct interpolation is found to be a viable method to drive air quality models.
Linghan Zeng, Jack Dibb, Eric Scheuer, Joseph M. Katich, Joshua P. Schwarz, Ilann Bourgeois, Jeff Peischl, Tom Ryerson, Carsten Warneke, Anne E. Perring, Glenn S. Diskin, Joshua P. DiGangi, John B. Nowak, Richard H. Moore, Elizabeth B. Wiggins, Demetrios Pagonis, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Lu Xu, and Rodney J. Weber
Atmos. Chem. Phys., 22, 8009–8036, https://doi.org/10.5194/acp-22-8009-2022, https://doi.org/10.5194/acp-22-8009-2022, 2022
Short summary
Short summary
Wildfires emit aerosol particles containing brown carbon material that affects visibility and global climate and is toxic. Brown carbon is poorly characterized due to measurement limitations, and its evolution in the atmosphere is not well known. We report on aircraft measurements of brown carbon from large wildfires in the western United States. We compare two methods for measuring brown carbon and study the evolution of brown carbon in the smoke as it moved away from the burning regions.
Katherine R. Travis, James H. Crawford, Gao Chen, Carolyn E. Jordan, Benjamin A. Nault, Hwajin Kim, Jose L. Jimenez, Pedro Campuzano-Jost, Jack E. Dibb, Jung-Hun Woo, Younha Kim, Shixian Zhai, Xuan Wang, Erin E. McDuffie, Gan Luo, Fangqun Yu, Saewung Kim, Isobel J. Simpson, Donald R. Blake, Limseok Chang, and Michelle J. Kim
Atmos. Chem. Phys., 22, 7933–7958, https://doi.org/10.5194/acp-22-7933-2022, https://doi.org/10.5194/acp-22-7933-2022, 2022
Short summary
Short summary
The 2016 Korea–United States Air Quality (KORUS-AQ) field campaign provided a unique set of observations to improve our understanding of PM2.5 pollution in South Korea. Models typically have errors in simulating PM2.5 in this region, which is of concern for the development of control measures. We use KORUS-AQ observations to improve our understanding of the mechanisms driving PM2.5 and the implications of model errors for determining PM2.5 that is attributable to local or foreign sources.
Meloë S. F. Kacenelenbogen, Qian Tan, Sharon P. Burton, Otto P. Hasekamp, Karl D. Froyd, Yohei Shinozuka, Andreas J. Beyersdorf, Luke Ziemba, Kenneth L. Thornhill, Jack E. Dibb, Taylor Shingler, Armin Sorooshian, Reed W. Espinosa, Vanderlei Martins, Jose L. Jimenez, Pedro Campuzano-Jost, Joshua P. Schwarz, Matthew S. Johnson, Jens Redemann, and Gregory L. Schuster
Atmos. Chem. Phys., 22, 3713–3742, https://doi.org/10.5194/acp-22-3713-2022, https://doi.org/10.5194/acp-22-3713-2022, 2022
Short summary
Short summary
The impact of aerosols on Earth's radiation budget and human health is important and strongly depends on their composition. One desire of our scientific community is to derive the composition of the aerosol from satellite sensors. However, satellites observe aerosol optical properties (and not aerosol composition) based on remote sensing instrumentation. This study assesses how much aerosol optical properties can tell us about aerosol composition.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-7, https://doi.org/10.5194/tc-2022-7, 2022
Manuscript not accepted for further review
Short summary
Short summary
This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
Dongwook Kim, Changmin Cho, Seokhan Jeong, Soojin Lee, Benjamin A. Nault, Pedro Campuzano-Jost, Douglas A. Day, Jason C. Schroder, Jose L. Jimenez, Rainer Volkamer, Donald R. Blake, Armin Wisthaler, Alan Fried, Joshua P. DiGangi, Glenn S. Diskin, Sally E. Pusede, Samuel R. Hall, Kirk Ullmann, L. Gregory Huey, David J. Tanner, Jack Dibb, Christoph J. Knote, and Kyung-Eun Min
Atmos. Chem. Phys., 22, 805–821, https://doi.org/10.5194/acp-22-805-2022, https://doi.org/10.5194/acp-22-805-2022, 2022
Short summary
Short summary
CHOCHO was simulated using a 0-D box model constrained by measurements during the KORUS-AQ mission. CHOCHO concentration was high in large cities, aromatics being the most important precursors. Loss path to aerosol was the highest sink, contributing to ~ 20 % of secondary organic aerosol formation. Our work highlights that simple CHOCHO surface uptake approach is valid only for low aerosol conditions and more work is required to understand CHOCHO solubility in high-aerosol conditions.
Shixian Zhai, Daniel J. Jacob, Jared F. Brewer, Ke Li, Jonathan M. Moch, Jhoon Kim, Seoyoung Lee, Hyunkwang Lim, Hyun Chul Lee, Su Keun Kuk, Rokjin J. Park, Jaein I. Jeong, Xuan Wang, Pengfei Liu, Gan Luo, Fangqun Yu, Jun Meng, Randall V. Martin, Katherine R. Travis, Johnathan W. Hair, Bruce E. Anderson, Jack E. Dibb, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jung-Hun Woo, Younha Kim, Qiang Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 16775–16791, https://doi.org/10.5194/acp-21-16775-2021, https://doi.org/10.5194/acp-21-16775-2021, 2021
Short summary
Short summary
Geostationary satellite aerosol optical depth (AOD) has tremendous potential for monitoring surface fine particulate matter (PM2.5). Our study explored the physical relationship between AOD and PM2.5 by integrating data from surface networks, aircraft, and satellites with the GEOS-Chem chemical transport model. We quantitatively showed that accurate simulation of aerosol size distributions, boundary layer depths, relative humidity, coarse particles, and diurnal variations in PM2.5 are essential.
Charles A. Brock, Karl D. Froyd, Maximilian Dollner, Christina J. Williamson, Gregory Schill, Daniel M. Murphy, Nicholas J. Wagner, Agnieszka Kupc, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jason C. Schroder, Douglas A. Day, Derek J. Price, Bernadett Weinzierl, Joshua P. Schwarz, Joseph M. Katich, Siyuan Wang, Linghan Zeng, Rodney Weber, Jack Dibb, Eric Scheuer, Glenn S. Diskin, Joshua P. DiGangi, ThaoPaul Bui, Jonathan M. Dean-Day, Chelsea R. Thompson, Jeff Peischl, Thomas B. Ryerson, Ilann Bourgeois, Bruce C. Daube, Róisín Commane, and Steven C. Wofsy
Atmos. Chem. Phys., 21, 15023–15063, https://doi.org/10.5194/acp-21-15023-2021, https://doi.org/10.5194/acp-21-15023-2021, 2021
Short summary
Short summary
The Atmospheric Tomography Mission was an airborne study that mapped the chemical composition of the remote atmosphere. From this, we developed a comprehensive description of aerosol properties that provides a unique, global-scale dataset against which models can be compared. The data show the polluted nature of the remote atmosphere in the Northern Hemisphere and quantify the contributions of sea salt, dust, soot, biomass burning particles, and pollution particles to the haziness of the sky.
Linghan Zeng, Amy P. Sullivan, Rebecca A. Washenfelder, Jack Dibb, Eric Scheuer, Teresa L. Campos, Joseph M. Katich, Ezra Levin, Michael A. Robinson, and Rodney J. Weber
Atmos. Meas. Tech., 14, 6357–6378, https://doi.org/10.5194/amt-14-6357-2021, https://doi.org/10.5194/amt-14-6357-2021, 2021
Short summary
Short summary
Three online systems for measuring water-soluble brown carbon are compared. A mist chamber and two different particle-into-liquid samplers were deployed on separate research aircraft targeting wildfires and followed a similar detection method using a long-path liquid waveguide with a spectrometer to measure the light absorption from 300 to 700 nm. Detection limits, signal hysteresis and other sampling issues are compared, and further improvements of these liquid-based systems are provided.
Jiajue Chai, Jack E. Dibb, Bruce E. Anderson, Claire Bekker, Danielle E. Blum, Eric Heim, Carolyn E. Jordan, Emily E. Joyce, Jackson H. Kaspari, Hannah Munro, Wendell W. Walters, and Meredith G. Hastings
Atmos. Chem. Phys., 21, 13077–13098, https://doi.org/10.5194/acp-21-13077-2021, https://doi.org/10.5194/acp-21-13077-2021, 2021
Short summary
Short summary
Nitrous acid (HONO) derived from wildfire emissions plays a key role in controlling atmospheric oxidation chemistry. However, the HONO budget remains poorly constrained. By combining the field-observed concentrations and novel isotopic composition (N and O) of HONO and nitrogen oxides (NOx), we quantitatively constrained the relative contribution of each pathway to secondary HONO production and the relative importance of major atmospheric oxidants (ozone versus peroxy) in aged wildfire smoke.
Hongyu Guo, Pedro Campuzano-Jost, Benjamin A. Nault, Douglas A. Day, Jason C. Schroder, Dongwook Kim, Jack E. Dibb, Maximilian Dollner, Bernadett Weinzierl, and Jose L. Jimenez
Atmos. Meas. Tech., 14, 3631–3655, https://doi.org/10.5194/amt-14-3631-2021, https://doi.org/10.5194/amt-14-3631-2021, 2021
Short summary
Short summary
We utilize a set of high-quality datasets collected during the NASA Atmospheric Tomography Mission to investigate the impact of differences in observable particle sizes across aerosol instruments in aerosol measurement comparisons. Very good agreement was found between chemically and physically derived submicron aerosol volume. Results support a lack of significant unknown biases in the response of an Aerodyne aerosol mass spectrometer (AMS) when sampling remote aerosols across the globe.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
Short summary
Short summary
This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Melinda K. Schueneman, Benjamin A. Nault, Pedro Campuzano-Jost, Duseong S. Jo, Douglas A. Day, Jason C. Schroder, Brett B. Palm, Alma Hodzic, Jack E. Dibb, and Jose L. Jimenez
Atmos. Meas. Tech., 14, 2237–2260, https://doi.org/10.5194/amt-14-2237-2021, https://doi.org/10.5194/amt-14-2237-2021, 2021
Short summary
Short summary
This work focuses on two important properties of the aerosol, acidity, and sulfate composition, which is important for our understanding of aerosol health and environmental impacts. We explore different methods to understand the composition of the aerosol with measurements from a specific instrument and apply those methods to a large dataset. These measurements are confounded by other factors, making it challenging to predict aerosol sulfate composition; pH estimations, however, show promise.
Benjamin A. Nault, Pedro Campuzano-Jost, Douglas A. Day, Hongyu Guo, Duseong S. Jo, Anne V. Handschy, Demetrios Pagonis, Jason C. Schroder, Melinda K. Schueneman, Michael J. Cubison, Jack E. Dibb, Alma Hodzic, Weiwei Hu, Brett B. Palm, and Jose L. Jimenez
Atmos. Meas. Tech., 13, 6193–6213, https://doi.org/10.5194/amt-13-6193-2020, https://doi.org/10.5194/amt-13-6193-2020, 2020
Short summary
Short summary
Collecting particulate matter, or aerosols, onto filters to be analyzed offline is a widely used method to investigate the mass concentration and chemical composition of the aerosol, especially the inorganic portion. Here, we show that acidic aerosol (sulfuric acid) collected onto filters and then exposed to high ammonia mixing ratios (from human emissions) will lead to biases in the ammonium collected onto filters, and the uptake of ammonia is rapid (< 10 s), which impacts the filter data.
Cited articles
Balaji, V., Boville, B., Cheung, S., Clune, T., Collins, N., Craig, T., Cruz, C., da Silva, A., DeLuca, C., de Fainchtein, R., Dunlap, R., Eaton, B., Goldhaber, S., Hallberg, B., Henderson, T., Hill, C., Iredell, M., Jacob, J., Jacob, R., Jones, P., Kauffman, B., Kluzek, E., Koziol, B., Larson, J., Li, P., Liu, F., Michalakes, J., Montuoro, R., Murphy, S., Neckels, D., O Kuinghttons, R., Oehmke, B., Panaccione, C., Rosen, D., Rosinski, J., Rothstein, M., Saint, K., Sawyer, W., Schwab, E., Smithline, S., Spector, W., Stark, D., Suarez, M., Swift, S., Theurich, G., Trayanov, A., Vasquez, S., Wolfe, J., Yang, W., Young, M., and Zaslavsky, L.: ESMF User Guide, Tech. rep., https://earthsystemmodeling.org/docs/release/ESMF_8_1_1/ESMF_refdoc.pdf (last access: 8 September 2024), 2021. a
Bonne, J., Steen-Larsen, H. C. ., Risi, C., Werner, M., Sodemann, H., Lacour, J., Fettweis, X., Cesana, G., Delmotte, M., Cattani, O., Vallelonga, P., Kjær, H. A., Clerbaux, C., Sveinbjörnsdóttir, Á. E., and Masson-Delmotte, V.: The summer 2012 Greenland Heat Wave: In situ and remote sensing observations of water vapor isotopic composition during an Atmospheric River Event, J. Geophys. Res.-Atmos., 120, 2970–2989, https://doi.org/10.1002/2014jd022602, 2015. a, b
Box, J. E., Wehrlé, A., van As, D., Fausto, R. S., Kjeldsen, K. K., Dachauer, A., Ahlstrøm, A. P., and Picard, G.: Greenland ice sheet rainfall, heat and albedo feedback impacts from the mid-august 2021 Atmospheric River, Geophys. Res. Lett., 49, e2021GL097356, https://doi.org/10.1029/2021gl097356, 2022. a, b, c
Box, J. E., Nielsen, K. P., Yang, X., Niwano, M., Wehrlé, A., van As, D., Fettweis, X., Køltzow, Morten A. Ø., Palmason, B., Fausto, R. S., van den Broeke, M. R., Huai, B., Ahlstrøm, A. P., Langley, K., Dachauer, A., and Noël, B.: Greenland ice sheet rainfall climatology, extremes and Atmospheric River Rapids, Meteorol. Appl., 30, e2134, https://doi.org/10.1002/met.2134, 2023. a, b, c
Bresson, H., Rinke, A., Mech, M., Reinert, D., Schemann, V., Ebell, K., Maturilli, M., Viceto, C., Gorodetskaya, I., and Crewell, S.: Case study of a moisture intrusion over the Arctic with the ICOsahedral Non-hydrostatic (ICON) model: resolution dependence of its representation, Atmos. Chem. Phys., 22, 173–196, https://doi.org/10.5194/acp-22-173-2022, 2022. a, b, c
C3S: European State of the Climate 2022, Full report, https://climate.copernicus.eu/ESOTC/2022 (last access: 8 September 2024), 2023. a
Computational and Information Systems Laboratory: Cheyenne: HPE/SGI ICE XA System (Climate Simulation Laboratory), Boulder, CO: National Center for Atmospheric Research, https://doi.org/10.5065/D6RX99HX, 2017. a
Copernicus, C.: ERA5 monthly averaged data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.6860a573, 2019. a
Craig, C., Bacmeister, J., Callaghan, P., Eaton, B., Gettelman, A., Goldhaber, S. N., Hannay, C., Herrington, A., Lauritzen, P. H., McInerney, J., Medeiros, B., Mills, M. J., Neale, R., Tilmes, S., Truesdale, J., Vertenstein, M., and Vitt, F. M.: CAM6.3 User's Guide, Tech. rep., NCAR/TN-571+EDD, https://doi.org/10.5065/Z953-ZC95, 2021. a
Curry, C. L., Islam, S. U., Zwiers, F. W., and Déry, S. J.: Atmospheric rivers increase future flood risk in western Canada’s largest Pacific River, Geophys. Res. Lett., 46, 1651–1661, https://doi.org/10.1029/2018gl080720, 2019. a
Danielson, J. and Gesch, D.: Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), Open-file report 2011-1073, U.S. Geological Survey, http://pubs.usgs.gov/of/2011/1073/pdf/of2011-1073.pdf (last access: 8 September 2024), 2011. a
ECMWF: IFS Documentation CY48R1 – Part III: Dynamics and Numerical Procedures, 3, ECMWF, https://doi.org/10.21957/26f0ad3473, 2023. a
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
Ettema, J., van den Broeke, M. R., van Meijgaard, E., van de Berg, W. J., Bamber, J. L., Box, J. E., and Bales, R. C.: Higher surface mass balance of the Greenland ice sheet revealed by high-resolution climate modeling, Geophys. Res. Lett., 36, L12501, https://doi.org/10.1029/2009GL038110, 2009. a, b
Franco, B., Fettweis, X., Lang, C., and Erpicum, M.: Impact of spatial resolution on the modelling of the Greenland ice sheet surface mass balance between 1990–2010, using the regional climate model MAR, The Cryosphere, 6, 695–711, https://doi.org/10.5194/tc-6-695-2012, 2012. a
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
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
Gettelman, A., Hannay, C., Bacmeister, J. T., Neale, R. B., Pendergrass, A. G., Danabasoglu, G., Lamarque, J.-F., Fasullo, J. T., Bailey, D. A., Lawrence, D. M., and Mills, M. J.: High climate sensitivity in the Community Earth System Model version 2 (CESM2), Geophys. Res. Lett., 46, 8329–8337, 2019. a
Hagos, S., Leung, L. R., Yang, Q., Zhao, C., and Lu, H.: Resolution and Dynamical Core Dependence of Atmospheric River Frequency in Global Model Simulations, J. Climate, 28, 2764–2776, https://doi.org/10.1175/JCLI-D-14-00567.1, 2015. a
Hagos, S. M., Leung, L. R., Yoon, J., Lu, J., and Gao, Y.: A projection of changes in landfalling atmospheric river frequency and extreme precipitation over western North America from the large ensemble CESM simulations, Geophys. Res. Lett., 43, 1357–1363, https://doi.org/10.1002/2015gl067392, 2016. a
Herrington, A. R., Lauritzen, P. H., Reed, K. A., Goldhaber, S., and Eaton, B. E.: Exploring a lower-resolution physics grid in CAM-SE-CSLAM, J. Adv. Model. Earth Sy., 11, 1894–1916, https://doi.org/10.1029/2019MS001684, 2019.
Herrington, A. R., Lauritzen, P. H., Lofverstrom, M., Lipscomb, W. H., Gettelman, A., and Taylor, M. A.: Impact of grids and dynamical cores in CESM2.2 on the surface mass balance of the Greenland Ice Sheet, J. Adv. Model. Earth Sy., 14, e2022MS003192, https://doi.org/10.1029/2022ms003192, 2022. a, b, c, d, e, f, g, h, i
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Huang, X., Rhoades, A. M., Ullrich, P. A., and Zarzycki, C. M.: An evaluation of the variable-resolution CESM for modeling California’s climate, J. Adv. Model. Earth Sy., 8, 345–369, https://doi.org/10.1002/2015MS000559, 2016. a, b
Huang, X., Swain, D. L., and Hall, A. D.: Future precipitation increase from very high resolution ensemble downscaling of Extreme Atmospheric River Storms in California, Science Advances, 6, eaba1323, https://doi.org/10.1126/sciadv.aba1323, 2020. a, b
Hurrell, J. W., Hack, J. J., Shea, D., Caron, J. M., and Rosinski, J.: A new sea surface temperature and sea ice boundary dataset for the Community Atmosphere Model, J. Climate, 21, 5145–5153, 2008. a
Ikeda, K., Rasmussen, R., Liu, C., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Miller, K., and Arsenault, K.: Simulation of seasonal snowfall over Colorado, Atmos. Res., 97, 462–477, https://doi.org/10.1016/j.atmosres.2010.04.010, 2010. a
Ikeda, K., Rasmussen, R., Liu, C., Newman, A., Chen, F., Barlage, M., Gutmann, E., Dudhia, J., Dai, A., Luce, C., and Musselman, K.: Snowfall and snowpack in the Western US as captured by convection-permitting climate simulations: current climate and pseudo global warming future climate, Clim. Dynam., 57, 2191–2215, https://doi.org/10.1007/s00382-021-05805-w, 2021. a
Kirbus, B., Tiedeck, S., Camplani, A., Chylik, J., Crewell, S., Dahlke, S., Ebell, K., Gorodetskaya, I., Griesche, H., Handorf, D., Höschel, I., Lauer, M., Neggers, R., Rückert, J., Shupe, M.D., Spreen, G., Walbröl, A., Wendisch, M., and Rinke, A.: Surface impacts and associated mechanisms of a moisture intrusion into the Arctic observed in mid-April 2020 during mosaic, Front. Earth Sci., 11, 1147848, https://doi.org/10.3389/feart.2023.1147848, 2023. a, b
Komatsu, K. K., Alexeev, V. A., Repina, I. A., and Tachibana, Y.: Poleward upgliding siberian atmospheric rivers over sea ice heat up Arctic Upper Air, Sci. Rep., 8, 2872, https://doi.org/10.1038/s41598-018-21159-6, 2018. a
Lauer, M., Rinke, A., Gorodetskaya, I., Sprenger, M., Mech, M., and Crewell, S.: Influence of atmospheric rivers and associated weather systems on precipitation in the Arctic, Atmos. Chem. Phys., 23, 8705–8726, https://doi.org/10.5194/acp-23-8705-2023, 2023.
Lauritzen, P. H., Bacmeister, J. T., Callaghan, P. F., and Taylor, M. A.: NCAR_Topo (v1.0): NCAR global model topography generation software for unstructured grids, Geosci. Model Dev., 8, 3975–3986, https://doi.org/10.5194/gmd-8-3975-2015, 2015. a, b
Lauritzen, P. H., Nair, R. D., Herrington, A. R., Callaghan, P., Goldhaber, S., Dennis, J. M., Bacmeister, J. T., Eaton, B. E., Zarzycki, C. M., Taylor, M. A., Ullrich, P. A., Dubos, T., Gettelman, A., Neale, R. B., Dobbins, B., Reed, K. A., Hannay, C., Medeiros, B., Benedict, J. J., and Tribbia, J. J.: NCAR release of Cam-SE in Cesm2.0: A reformulation of the spectral element dynamical core in dry-mass vertical coordinates with comprehensive treatment of condensates and Energy, J. Adv. Model. Earth Sy., 10, 1537–1570, https://doi.org/10.1029/2017ms001257, 2018. a
Lavers, D. A., Ralph, F. M., Waliser, D. E., Gershunov, A., and Dettinger, M. D.: Climate change intensification of horizontal water vapor transport in CMIP5, Geophys. Res. Lett., 42, 5617–5625, https://doi.org/10.1002/2015gl064672, 2015. a
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., van Kampenhout, L., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., van den Broeke, M., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M., Fox, A. M., Gentine, P., Hoffman, F., Keppel‐Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Val Martin, M., and Zeng, X.: The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, 2019. a
Lipscomb, W. H., Price, S. F., Hoffman, M. J., Leguy, G. R., Bennett, A. R., Bradley, S. L., Evans, K. J., Fyke, J. G., Kennedy, J. H., Perego, M., Ranken, D. M., Sacks, W. J., Salinger, A. G., Vargo, L. J., and Worley, P. H.: Description and evaluation of the Community Ice Sheet Model (CISM) v2.1, Geosci. Model Dev., 12, 387–424, https://doi.org/10.5194/gmd-12-387-2019, 2019. a
Marquardt Collow, A. B., Shields, C. A., Guan, B., Kim, S., Lora, J. M., McClenny, E. E., Nardi, K., Payne, A., Reid, K., Shearer, E. J., Tomé, R., Wille, J. D., Ramos, A. M., Gorodetskaya, I. V., Leung, L. R., O'Brien, T. A., Ralph, F. M., Rutz, J., Ullrich, P. A., and Wehner, M.: An overview of ARTMIP’s tier 2 reanalysis intercomparison: Uncertainty in the detection of atmospheric rivers and their associated precipitation, J. Geophys. Res.-Atmos., 127, e2021JD036155, https://doi.org/10.1029/2021jd036155, 2022. a
Mattingly, K. S., Mote, T. L., and Fettweis, X.: Atmospheric River impacts on Greenland Ice Sheet Surface Mass Balance, J. Geophys. Res.-Atmos., 123, 8538–8560, https://doi.org/10.1029/2018jd028714, 2018. a, b
Mattingly, K. S., Mote, T. L., Fettweis, X., van As, D., Van Tricht, K., Lhermitte, S., Pettersen, C., and Fausto, R. S.: Strong summer atmospheric rivers trigger greenland ice sheet melt through spatially varying surface energy balance and cloud regimes, J. Climate, 33, 6809–6832, https://doi.org/10.1175/jcli-d-19-0835.1, 2020. a, b, c, d
McClenny, E. E., Ullrich, P. A., and Grotjahn, R.: Sensitivity of atmospheric river vapor transport and precipitation to uniform sea surface temperature increases, J. Geophys. Res.-Atmos., 125, e2020JD033421, https://doi.org/10.1029/2020jd033421, 2020. a
Neff, W., Compo, G. P., Ralph, F. M., and Shupe, M. P.: Continental heat anomalies and the extreme melting of the Greenland ice surface in 2012 and 1889, J. Geophys. Res.-Atmos., 119, 6520–6536, https://doi.org/10.1002/2014JD021470, 2014. a, b
Noël, B., van de Berg, W. J., van Wessem, J. M., van Meijgaard, E., van As, D., Lenaerts, J. T. M., Lhermitte, S., Kuipers Munneke, P., Smeets, C. J. P. P., van Ulft, L. H., van de Wal, R. S. W., and van den Broeke, M. R.: Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 1: Greenland (1958–2016), The Cryosphere, 12, 811–831, https://doi.org/10.5194/tc-12-811-2018, 2018. a, b, c
Patricola, C., O’Brien, J., Risser, M., Rhoades, A., O’Brien, T., Ullrich, P., Stone, D., and Collins, W.: aximizing 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, b
Payne, A. E., Demory, M. E., Leung, L. R., Ramos, A. M., Shields, C. A., Rutz, J. L., Siler, N., Villarini, G., Hall, A., and Ralph, F. M.: Responses and impacts of atmospheric rivers to climate change, Nature Reviews Earth and Environment, 1, 143–157, https://doi.org/10.1038/s43017-020-0030-5, 2020. a, b
Pollard, D.: Comparisons of ice-sheet surface mass budgets from Paleoclimate Modeling Intercomparison Project (PMIP) simulations, Global Planet. Change, 24, 79–106, 2000. a
Rhoades, A. M., Jones, A. D., O’Brien, T. A., O’Brien, J. P., Ullrich, P. A., and Zarzycki, C. M.: Influences of North Pacific Ocean domain extent on the western U.S. winter hydroclimatology in variable-Resolution CESM, J. Geophys. Res.-Atmos., 125, e2019JD031977, https://doi.org/10.1029/2019jd031977, 2020a. a, b
Rhoades, A. M., Jones, A. D., Srivastava, A., Huang, H., O’Brien, T. A., Patricola, C. M., Ullrich, P. A., Wehner, M., and Zhou, Y.: The Shifting Scales of western U.S. landfalling atmospheric rivers under climate change, Geophys. Res. Lett., 47, e2020GL089096, https://doi.org/10.1029/2020gl089096, 2020b. 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, M., 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., 124, 13777–13802, https://doi.org/10.1029/2019jd030936, 2019. 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
Shields, C. A., Wille, J. D., Marquardt Collow, A. B., Maclennan, M., and Gorodetskaya, I. V.: Evaluating uncertainty and modes of variability for Antarctic Atmospheric Rivers, Geophys. Res. Lett., 49, e2022GL099577, https://doi.org/10.1029/2022gl099577, 2022. a
Shields, C. A., Payne, A. E., Shearer, E. J., Wehner, M. F., O’Brien, T. A., Rutz, J. J., Leung, L. R., Ralph, F. M., Collow, A. B., Ullrich, P. A., Dong, Q., Gershunov, A., Griffith, H., Guan, B., Lora, J. M., Lu, M., McClenny, E., Nardi, K. M., Pan, M., Qian, Y., Ramos, A. M., Shulgina, T., Viale, M., Sarangi, C., Tomé, R., and Zarzycki, C.: Future atmospheric rivers and impacts on precipitation: Overview of the ARTMIP Tier 2 high-resolution global warming experiment, Geophysi. Res. Lett., 50, e2022GL102091, https://doi.org/10.1029/2022GL102091, 2023.
Ullrich, P. A. and Taylor, M. A.: Arbitrary-Order Conservative and Consistent Remapping and a Theory of Linear Maps: Part I, Mon. Weather Rev., 143, 2419–2440, https://doi.org/10.1175/MWR-D-14-00343.1, 2015. a
Ullrich, P. A., Zarzycki, C. M., McClenny, E. E., Pinheiro, M. C., Stansfield, A. M., and Reed, K. A.: TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets, Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, 2021. a, b, c
van Kampenhout, L., Lenaerts, J. T., Lipscomb, W. H., Lhermitte, S., Noël, B., Vizcaíno, M., Sacks, W. J., and van den Broeke, M. R.: Present-day Greenland Ice Sheet climate and surface mass balance in CESM2, J. Geophys. Res.-Earth, 125, e2019JF005318, https://doi.org/10.1029/2019JF005318, 2020. a
Viceto, C., Gorodetskaya, I. V., Rinke, A., Maturilli, M., Rocha, A., and Crewell, S.: Atmospheric rivers and associated precipitation patterns during the ACLOUD and PASCAL campaigns near Svalbard (May–June 2017): case studies using observations, reanalyses, and a regional climate model, Atmos. Chem. Phys., 22, 441–463, https://doi.org/10.5194/acp-22-441-2022, 2022. a, b
Waling, A. and Herrington, A.: Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet, Zenodo [code and data set], https://doi.org/10.5281/zenodo.13738307, 2024.
Zarzycki, C. M. and Jablonowski, C.: Experimental tropical cyclone forecasts using a variable-resolution global model, Mon. Weather Rev., 143, 4012–4037, https://doi.org/10.1175/mwr-d-15-0159.1, 2015. a
Zarzycki, C. M., Jablonowski, C., Thatcher, D. R., and Taylor, M. A.: Effects of localized grid refinement on the general circulation and climatology in the community atmosphere model, J. Climate, 28, 2777–2803, https://doi.org/10.1175/jcli-d-14-00599.1, 2015. a
Zhang, P., Chen, G., Ma, W., Ming, Y., and Wu, Z.: Robust Atmospheric River response to global warming in idealized and comprehensive climate models, J. Climate, 34, 7717–7734, https://doi.org/10.1175/jcli-d-20-1005.1, 2021. a
Zhang, P., Chen, G., Ting, M., Ruby Leung, L., Guan, B., and Li, L.: More frequent atmospheric rivers slow the seasonal recovery of Arctic Sea Ice, Nat. Clim. Change, 13, 266–273, https://doi.org/10.1038/s41558-023-01599-3, 2023. a
Zhou, Y., O’Brien, T. A., Collins, W. D., Shields, C. A., Loring, B., and Elbashandy, A. A.: Characteristics and variability of winter northern Pacific Atmospheric River Flavors, J. Geophys. Res.-Atmos., 127, e2022JD037105, https://doi.org/10.1029/2022jd037105, 2022. a, b
Short summary
Atmospheric rivers (ARs) are channel-shaped features within the atmosphere that carry moisture from the mid-latitudes to the poles, bringing warm temperatures and moisture that can cause melt in the Arctic. We used variable-resolution grids to model ARs around the Greenland ice sheet and compared this output to uniform-resolution grids and reanalysis products. We found that the variable-resolution grids produced ARs and precipitation that were more similar to observation-based products.
Atmospheric rivers (ARs) are channel-shaped features within the atmosphere that carry moisture...
