Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-43-2021
© Author(s) 2021. 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-14-43-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0)
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
Adrian J. McDonald
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
Olaf Morgenstern
National Institute of Water & Atmospheric Research (NIWA), Wellington, New Zealand
Richard Querel
National Institute of Water & Atmospheric Research (NIWA), Lauder, New Zealand
Israel Silber
Department of Meteorology and Atmospheric Science, Pennsylvania State University, PA, USA
Connor J. Flynn
School of Meteorology, University of Oklahoma, Norman, OK, USA
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The nextGEMS project developed two Earth system models that resolve processes of the order of 10 km, giving more fidelity to the representation of local phenomena, globally. In its fourth cycle, nextGEMS performed simulations with coupled ocean, land, and atmosphere over the 2020–2049 period under the SSP3-7.0 scenario. Here, we provide an overview of nextGEMS, insights into the model development, and the realism of multi-decadal, kilometer-scale simulations.
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Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
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Israel Silber, Jennifer M. Comstock, Michael R. Kieburtz, and Lynn M. Russell
Earth Syst. Sci. Data, 17, 29–42, https://doi.org/10.5194/essd-17-29-2025, https://doi.org/10.5194/essd-17-29-2025, 2025
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We present ARMTRAJ, a set of multipurpose trajectory datasets, which augments cloud, aerosol, and boundary layer studies utilizing the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility data. ARMTRAJ data include ensemble run statistics that enhance consistency and serve as uncertainty metrics for air mass coordinates and state variables. ARMTRAJ will soon become a near real-time product that will accompany past, ongoing, and future ARM deployments.
Robin Björklund, Corinne Vigouroux, Peter Effertz, Omaira E. García, Alex Geddes, James Hannigan, Koji Miyagawa, Michael Kotkamp, Bavo Langerock, Gerald Nedoluha, Ivan Ortega, Irina Petropavlovskikh, Deniz Poyraz, Richard Querel, John Robinson, Hisako Shiona, Dan Smale, Penny Smale, Roeland Van Malderen, and Martine De Mazière
Atmos. Meas. Tech., 17, 6819–6849, https://doi.org/10.5194/amt-17-6819-2024, https://doi.org/10.5194/amt-17-6819-2024, 2024
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Different ground-based ozone measurements from the last 2 decades at Lauder are compared to each other. We want to know why different trends have been observed in the stratosphere. Also, the quality and relevance of tropospheric datasets need to be evaluated. While remaining drifts are still present, our study explains roughly half of the differences in observed trends in previous studies and shows the necessity for continuous review and improvement of the measurements.
Yunqian Zhu, Hideharu Akiyoshi, Valentina Aquila, Elisabeth Asher, Ewa M. Bednarz, Slimane Bekki, Christoph Brühl, Amy H. Butler, Parker Case, Simon Chabrillat, Gabriel Chiodo, Margot Clyne, Lola Falletti, Peter R. Colarco, Eric Fleming, Andrin Jörimann, Mahesh Kovilakam, Gerbrand Koren, Ales Kuchar, Nicolas Lebas, Qing Liang, Cheng-Cheng Liu, Graham Mann, Michael Manyin, Marion Marchand, Olaf Morgenstern, Paul Newman, Luke D. Oman, Freja F. Østerstrøm, Yifeng Peng, David Plummer, Ilaria Quaglia, William Randel, Samuel Rémy, Takashi Sekiya, Stephen Steenrod, Timofei Sukhodolov, Simone Tilmes, Kostas Tsigaridis, Rei Ueyama, Daniele Visioni, Xinyue Wang, Shingo Watanabe, Yousuke Yamashita, Pengfei Yu, Wandi Yu, Jun Zhang, and Zhihong Zhuo
EGUsphere, https://doi.org/10.5194/egusphere-2024-3412, https://doi.org/10.5194/egusphere-2024-3412, 2024
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To understand the climate impact of the 2022 Hunga volcanic eruption, we developed a climate model-observation comparison project. The paper describes the protocols and models that participate in the experiments. We designed several experiments to achieve our goal of this activity: 1. evaluate the climate model performance; 2. understand the Earth system responses to this eruption.
Abigail S. Williams, Jeramy L. Dedrick, Lynn M. Russell, Florian Tornow, Israel Silber, Ann M. Fridlind, Benjamin Swanson, Paul J. DeMott, Paul Zieger, and Radovan Krejci
Atmos. Chem. Phys., 24, 11791–11805, https://doi.org/10.5194/acp-24-11791-2024, https://doi.org/10.5194/acp-24-11791-2024, 2024
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The measured aerosol size distribution modes reveal distinct properties characteristic of cold-air outbreaks in the Norwegian Arctic. We find higher sea spray number concentrations, smaller Hoppel minima, lower effective supersaturations, and accumulation-mode particle scavenging during cold-air outbreaks. These results advance our understanding of cold-air outbreak aerosol–cloud interactions in order to improve their accurate representation in models.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Evgueni Kassianov, Connor J. Flynn, James C. Barnard, Brian D. Ermold, and Jennifer M. Comstock
Atmos. Meas. Tech., 17, 4997–5013, https://doi.org/10.5194/amt-17-4997-2024, https://doi.org/10.5194/amt-17-4997-2024, 2024
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Olaf Morgenstern
Atmos. Chem. Phys., 24, 8105–8123, https://doi.org/10.5194/acp-24-8105-2024, https://doi.org/10.5194/acp-24-8105-2024, 2024
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I use errors in climate model simulations to derive correction factors for the impacts of greenhouse gases and particles that bring these simulated temperature fields into agreement with an observational reconstruction of the Earth's temperature. On average across eight models, a reduction by about one-half of the particle-induced cooling would be required, causing only 0.24 K of cooling since 1850–1899. The greenhouse gas warming simulated by several highly sensitive models would also reduce.
Nelson Bègue, Alexandre Baron, Gisèle Krysztofiak, Gwenaël Berthet, Corinna Kloss, Fabrice Jégou, Sergey Khaykin, Marion Ranaivombola, Tristan Millet, Thierry Portafaix, Valentin Duflot, Philippe Keckhut, Hélène Vérèmes, Guillaume Payen, Mahesh Kumar Sha, Pierre-François Coheur, Cathy Clerbaux, Michaël Sicard, Tetsu Sakai, Richard Querel, Ben Liley, Dan Smale, Isamu Morino, Osamu Uchino, Tomohiro Nagai, Penny Smale, John Robinson, and Hassan Bencherif
Atmos. Chem. Phys., 24, 8031–8048, https://doi.org/10.5194/acp-24-8031-2024, https://doi.org/10.5194/acp-24-8031-2024, 2024
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During the 2020 austral summer, the pristine atmosphere of the southwest Indian Ocean basin experienced significant perturbations. Numerical models indicated that the lower-stratospheric aerosol content was influenced by the intense and persistent stratospheric aerosol layer generated during the 2019–2020 extreme Australian bushfire events. Ground-based observations at Réunion confirmed the simultaneous presence of African and Australian aerosol layers.
Grégory V. Cesana, Olivia Pierpaoli, Matteo Ottaviani, Linh Vu, Zhonghai Jin, and Israel Silber
Atmos. Chem. Phys., 24, 7899–7909, https://doi.org/10.5194/acp-24-7899-2024, https://doi.org/10.5194/acp-24-7899-2024, 2024
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Better characterizing the relationship between sea ice and clouds is key to understanding Arctic climate because clouds and sea ice affect surface radiation and modulate Arctic surface warming. Our results indicate that Arctic liquid clouds robustly increase in response to sea ice decrease. This increase has a cooling effect on the surface because more solar radiation is reflected back to space, and it should contribute to dampening future Arctic surface warming.
Kelly A. Balmes, Laura D. Riihimaki, John Wood, Connor Flynn, Adam Theisen, Michael Ritsche, Lynn Ma, Gary B. Hodges, and Christian Herrera
Atmos. Meas. Tech., 17, 3783–3807, https://doi.org/10.5194/amt-17-3783-2024, https://doi.org/10.5194/amt-17-3783-2024, 2024
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A new hyperspectral radiometer (HSR1) was deployed and evaluated in the central United States (northern Oklahoma). The HSR1 total spectral irradiance agreed well with nearby existing instruments, but the diffuse spectral irradiance was slightly smaller. The HSR1-retrieved aerosol optical depth (AOD) also agreed well with other retrieved AODs. The HSR1 performance is encouraging: new hyperspectral knowledge is possible that could inform atmospheric process understanding and weather forecasting.
Guang Zeng, Richard Querel, Hisako Shiona, Deniz Poyraz, Roeland Van Malderen, Alex Geddes, Penny Smale, Dan Smale, John Robinson, and Olaf Morgenstern
Atmos. Chem. Phys., 24, 6413–6432, https://doi.org/10.5194/acp-24-6413-2024, https://doi.org/10.5194/acp-24-6413-2024, 2024
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We present a homogenised ozonesonde record (1987–2020) for Lauder, a Southern Hemisphere mid-latitude site; identify factors driving ozone trends; and attribute them to anthropogenic forcings using statistical analysis and model simulations. We find that significant negative lower-stratospheric ozone trends identified at Lauder are associated with an increase in tropopause height and that CO2-driven dynamical changes have played an increasingly important role in driving ozone trends.
Heesung Chong, Gonzalo González Abad, Caroline R. Nowlan, Christopher Chan Miller, Alfonso Saiz-Lopez, Rafael P. Fernandez, Hyeong-Ahn Kwon, Zolal Ayazpour, Huiqun Wang, Amir H. Souri, Xiong Liu, Kelly Chance, Ewan O'Sullivan, Jhoon Kim, Ja-Ho Koo, William R. Simpson, François Hendrick, Richard Querel, Glen Jaross, Colin Seftor, and Raid M. Suleiman
Atmos. Meas. Tech., 17, 2873–2916, https://doi.org/10.5194/amt-17-2873-2024, https://doi.org/10.5194/amt-17-2873-2024, 2024
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We present a new bromine monoxide (BrO) product derived using radiances measured from OMPS-NM on board the Suomi-NPP satellite. This product provides nearly a decade of global stratospheric and tropospheric column retrievals, a feature that is currently rare in publicly accessible datasets. Both stratospheric and tropospheric columns from OMPS-NM demonstrate robust performance, exhibiting good agreement with ground-based observations collected at three stations (Lauder, Utqiagvik, and Harestua).
Hiren T. Jethva, Omar Torres, Richard A. Ferrare, Sharon P. Burton, Anthony L. Cook, David B. Harper, Chris A. Hostetler, Jens Redemann, Vinay Kayetha, Samuel LeBlanc, Kristina Pistone, Logan Mitchell, and Connor J. Flynn
Atmos. Meas. Tech., 17, 2335–2366, https://doi.org/10.5194/amt-17-2335-2024, https://doi.org/10.5194/amt-17-2335-2024, 2024
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We introduce a novel synergy algorithm applied to ORALCES airborne measurements of above-cloud aerosol optical depth and UV–Vis satellite observations from OMI and MODIS to retrieve spectral aerosol single-scattering albedo of lofted layers of carbonaceous smoke aerosols over clouds. The development of the proposed aerosol–cloud algorithm implies a possible synergy of CALIOP and OMI–MODIS passive sensors to deduce a global product of AOD and SSA of absorbing aerosols above clouds.
Alexander Geddes, Ben Liley, Richard McKenzie, Michael Kotkamp, and Richard Querel
Atmos. Meas. Tech., 17, 827–838, https://doi.org/10.5194/amt-17-827-2024, https://doi.org/10.5194/amt-17-827-2024, 2024
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In this paper we describe a unique spectrometer that has been developed and tested over 10 years at Lauder, New Zealand. The spectrometer in question, UV2, makes alternating measurements of global UV and direct sun UV irradiance. After an assessment of the instrument performance, we compare the ozone and aerosol optical depth derived from UV2 to other independent measurements, finding excellent agreement suggesting that UV2 could supersede these measurements, particularly for ozone.
Yusuf A. Bhatti, Laura E. Revell, Alex J. Schuddeboom, Adrian J. McDonald, Alex T. Archibald, Jonny Williams, Abhijith U. Venugopal, Catherine Hardacre, and Erik Behrens
Atmos. Chem. Phys., 23, 15181–15196, https://doi.org/10.5194/acp-23-15181-2023, https://doi.org/10.5194/acp-23-15181-2023, 2023
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Aerosols are a large source of uncertainty over the Southern Ocean. A dominant source of sulfate aerosol in this region is dimethyl sulfide (DMS), which is poorly simulated by climate models. We show the sensitivity of simulated atmospheric DMS to the choice of oceanic DMS data set and emission scheme. We show that oceanic DMS has twice the influence on atmospheric DMS than the emission scheme. Simulating DMS more accurately in climate models will help to constrain aerosol uncertainty.
Zhangcheng Pei, Sonya L. Fiddes, W. John R. French, Simon P. Alexander, Marc D. Mallet, Peter Kuma, and Adrian McDonald
Atmos. Chem. Phys., 23, 14691–14714, https://doi.org/10.5194/acp-23-14691-2023, https://doi.org/10.5194/acp-23-14691-2023, 2023
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In this paper, we use ground-based observations to evaluate a climate model and a satellite product in simulating surface radiation and investigate how radiation biases are influenced by cloud properties over the Southern Ocean. We find that significant radiation biases exist in both the model and satellite. The cloud fraction and cloud occurrence play an important role in affecting radiation biases. We suggest further development for the model and satellite using ground-based observations.
Marina Friedel, Gabriel Chiodo, Timofei Sukhodolov, James Keeble, Thomas Peter, Svenja Seeber, Andrea Stenke, Hideharu Akiyoshi, Eugene Rozanov, David Plummer, Patrick Jöckel, Guang Zeng, Olaf Morgenstern, and Béatrice Josse
Atmos. Chem. Phys., 23, 10235–10254, https://doi.org/10.5194/acp-23-10235-2023, https://doi.org/10.5194/acp-23-10235-2023, 2023
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Previously, it has been suggested that springtime Arctic ozone depletion might worsen in the coming decades due to climate change, which might counteract the effect of reduced ozone-depleting substances. Here, we show with different chemistry–climate models that springtime Arctic ozone depletion will likely decrease in the future. Further, we explain why models show a large spread in the projected development of Arctic ozone depletion and use the model spread to constrain future projections.
McKenna W. Stanford, Ann M. Fridlind, Israel Silber, Andrew S. Ackerman, Greg Cesana, Johannes Mülmenstädt, Alain Protat, Simon Alexander, and Adrian McDonald
Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023, https://doi.org/10.5194/acp-23-9037-2023, 2023
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Clouds play an important role in the Earth’s climate system as they modulate the amount of radiation that either reaches the surface or is reflected back to space. This study demonstrates an approach to robustly evaluate surface-based observations against a large-scale model. We find that the large-scale model precipitates too infrequently relative to observations, contrary to literature documentation suggesting otherwise based on satellite measurements.
Jonny Williams, Erik Behrens, Olaf Morgenstern, Peter Gibson, and Joao Teixeira
EGUsphere, https://doi.org/10.5194/egusphere-2023-1694, https://doi.org/10.5194/egusphere-2023-1694, 2023
Preprint withdrawn
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We use open-source cyclone tracking software and state-of-the-art climate models to characterise present-day tropical cyclones – TCs – in the South Pacific before moving on to estimate how they may change in the future. A robust result of this work is the projection of future intensification of TCs. However, the question of their future occurrence frequency is less clear. Under extreme future warming scenarios, we postulate a possible increase in power dissipation per TC of up to 25 %.
Ian Chang, Lan Gao, Connor J. Flynn, Yohei Shinozuka, Sarah J. Doherty, Michael S. Diamond, Karla M. Longo, Gonzalo A. Ferrada, Gregory R. Carmichael, Patricia Castellanos, Arlindo M. da Silva, Pablo E. Saide, Calvin Howes, Zhixin Xue, Marc Mallet, Ravi Govindaraju, Qiaoqiao Wang, Yafang Cheng, Yan Feng, Sharon P. Burton, Richard A. Ferrare, Samuel E. LeBlanc, Meloë S. Kacenelenbogen, Kristina Pistone, Michal Segal-Rozenhaimer, Kerry G. Meyer, Ju-Mee Ryoo, Leonhard Pfister, Adeyemi A. Adebiyi, Robert Wood, Paquita Zuidema, Sundar A. Christopher, and Jens Redemann
Atmos. Chem. Phys., 23, 4283–4309, https://doi.org/10.5194/acp-23-4283-2023, https://doi.org/10.5194/acp-23-4283-2023, 2023
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Abundant aerosols are present above low-level liquid clouds over the southeastern Atlantic during late austral spring. The model simulation differences in the proportion of aerosol residing in the planetary boundary layer and in the free troposphere can greatly affect the regional aerosol radiative effects. This study examines the aerosol loading and fractional aerosol loading in the free troposphere among various models and evaluates them against measurements from the NASA ORACLES campaign.
Francesca Gallo, Janek Uin, Kevin J. Sanchez, Richard H. Moore, Jian Wang, Robert Wood, Fan Mei, Connor Flynn, Stephen Springston, Eduardo B. Azevedo, Chongai Kuang, and Allison C. Aiken
Atmos. Chem. Phys., 23, 4221–4246, https://doi.org/10.5194/acp-23-4221-2023, https://doi.org/10.5194/acp-23-4221-2023, 2023
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This study provides a summary statistic of multiday aerosol plume transport event influences on aerosol physical properties and the cloud condensation nuclei budget at the U.S. Department of Energy Atmospheric Radiation Measurement Facility in the eastern North Atlantic (ENA). An algorithm that integrates aerosol properties is developed and applied to identify multiday aerosol transport events. The influence of the aerosol plumes on aerosol populations at the ENA is successively assessed.
Ruhi S. Humphries, Melita D. Keywood, Jason P. Ward, James Harnwell, Simon P. Alexander, Andrew R. Klekociuk, Keiichiro Hara, Ian M. McRobert, Alain Protat, Joel Alroe, Luke T. Cravigan, Branka Miljevic, Zoran D. Ristovski, Robyn Schofield, Stephen R. Wilson, Connor J. Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Greg M. McFarquhar, Scott D. Chambers, Alastair G. Williams, and Alan D. Griffiths
Atmos. Chem. Phys., 23, 3749–3777, https://doi.org/10.5194/acp-23-3749-2023, https://doi.org/10.5194/acp-23-3749-2023, 2023
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Observations of aerosols in pristine regions are rare but are vital to constraining the natural baseline from which climate simulations are calculated. Here we present recent seasonal observations of aerosols from the Southern Ocean and contrast them with measurements from Antarctica, Australia and regionally relevant voyages. Strong seasonal cycles persist, but striking differences occur at different latitudes. This study highlights the need for more long-term observations in remote regions.
Udo Frieß, Karin Kreher, Richard Querel, Holger Schmithüsen, Dan Smale, Rolf Weller, and Ulrich Platt
Atmos. Chem. Phys., 23, 3207–3232, https://doi.org/10.5194/acp-23-3207-2023, https://doi.org/10.5194/acp-23-3207-2023, 2023
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Reactive bromine compounds, emitted by the sea ice during polar spring, play an important role in the atmospheric chemistry of the coastal regions of Antarctica. We investigate the sources and impacts of reactive bromine in detail using many years of measurements at two Antarctic sites located at opposite sides of the Antarctic continent. Using a multitude of meteorological observations, we were able to identify the main triggers and source regions for reactive bromine in Antarctica.
Peter Kuma, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland
Atmos. Chem. Phys., 23, 523–549, https://doi.org/10.5194/acp-23-523-2023, https://doi.org/10.5194/acp-23-523-2023, 2023
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We present a machine learning method for determining cloud types in climate model output and satellite observations based on ground observations of cloud genera. We analyse cloud type biases and changes with temperature in climate models and show that the bias is anticorrelated with climate sensitivity. Models simulating decreasing stratiform and increasing cumuliform clouds with increased CO2 concentration tend to have higher climate sensitivity than models simulating the opposite tendencies.
Paul A. Barrett, Steven J. Abel, Hugh Coe, Ian Crawford, Amie Dobracki, James Haywood, Steve Howell, Anthony Jones, Justin Langridge, Greg M. McFarquhar, Graeme J. Nott, Hannah Price, Jens Redemann, Yohei Shinozuka, Kate Szpek, Jonathan W. Taylor, Robert Wood, Huihui Wu, Paquita Zuidema, Stéphane Bauguitte, Ryan Bennett, Keith Bower, Hong Chen, Sabrina Cochrane, Michael Cotterell, Nicholas Davies, David Delene, Connor Flynn, Andrew Freedman, Steffen Freitag, Siddhant Gupta, David Noone, Timothy B. Onasch, James Podolske, Michael R. Poellot, Sebastian Schmidt, Stephen Springston, Arthur J. Sedlacek III, Jamie Trembath, Alan Vance, Maria A. Zawadowicz, and Jianhao Zhang
Atmos. Meas. Tech., 15, 6329–6371, https://doi.org/10.5194/amt-15-6329-2022, https://doi.org/10.5194/amt-15-6329-2022, 2022
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To better understand weather and climate, it is vital to go into the field and collect observations. Often measurements take place in isolation, but here we compared data from two aircraft and one ground-based site. This was done in order to understand how well measurements made on one platform compared to those made on another. Whilst this is easy to do in a controlled laboratory setting, it is more challenging in the real world, and so these comparisons are as valuable as they are rare.
Frederic Tridon, Israel Silber, Alessandro Battaglia, Stefan Kneifel, Ann Fridlind, Petros Kalogeras, and Ranvir Dhillon
Atmos. Chem. Phys., 22, 12467–12491, https://doi.org/10.5194/acp-22-12467-2022, https://doi.org/10.5194/acp-22-12467-2022, 2022
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The role of ice precipitation in the Earth water budget is not well known because ice particles are complex, and their formation involves intricate processes. Riming of ice crystals by supercooled water droplets is an efficient process, but little is known about its importance at high latitudes. In this work, by exploiting the deployment of an unprecedented number of remote sensing systems in Antarctica, we find that riming occurs at much lower temperatures compared with the mid-latitudes.
Sophie Godin-Beekmann, Niramson Azouz, Viktoria F. Sofieva, Daan Hubert, Irina Petropavlovskikh, Peter Effertz, Gérard Ancellet, Doug A. Degenstein, Daniel Zawada, Lucien Froidevaux, Stacey Frith, Jeannette Wild, Sean Davis, Wolfgang Steinbrecht, Thierry Leblanc, Richard Querel, Kleareti Tourpali, Robert Damadeo, Eliane Maillard Barras, René Stübi, Corinne Vigouroux, Carlo Arosio, Gerald Nedoluha, Ian Boyd, Roeland Van Malderen, Emmanuel Mahieu, Dan Smale, and Ralf Sussmann
Atmos. Chem. Phys., 22, 11657–11673, https://doi.org/10.5194/acp-22-11657-2022, https://doi.org/10.5194/acp-22-11657-2022, 2022
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An updated evaluation up to 2020 of stratospheric ozone profile long-term trends at extrapolar latitudes based on satellite and ground-based records is presented. Ozone increase in the upper stratosphere is confirmed, with significant trends at most latitudes. In this altitude region, a very good agreement is found with trends derived from chemistry–climate model simulations. Observed and modelled trends diverge in the lower stratosphere, but the differences are non-significant.
Samuel E. LeBlanc, Michal Segal-Rozenhaimer, Jens Redemann, Connor Flynn, Roy R. Johnson, Stephen E. Dunagan, Robert Dahlgren, Jhoon Kim, Myungje Choi, Arlindo da Silva, Patricia Castellanos, Qian Tan, Luke Ziemba, Kenneth Lee Thornhill, and Meloë Kacenelenbogen
Atmos. Chem. Phys., 22, 11275–11304, https://doi.org/10.5194/acp-22-11275-2022, https://doi.org/10.5194/acp-22-11275-2022, 2022
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Airborne observations of atmospheric particles and pollution over Korea during a field campaign in May–June 2016 showed that the smallest atmospheric particles are present in the lowest 2 km of the atmosphere. The aerosol size is more spatially variable than optical thickness. We show this with remote sensing (4STAR), in situ (LARGE) observations, satellite measurements (GOCI), and modeled properties (MERRA-2), and it is contrary to the current understanding.
Haochi Che, Michal Segal-Rozenhaimer, Lu Zhang, Caroline Dang, Paquita Zuidema, Arthur J. Sedlacek III, Xiaoye Zhang, and Connor Flynn
Atmos. Chem. Phys., 22, 8767–8785, https://doi.org/10.5194/acp-22-8767-2022, https://doi.org/10.5194/acp-22-8767-2022, 2022
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A 17-month in situ study on Ascension Island found low single-scattering albedo and strong absorption enhancement of the marine boundary layer aerosols during biomass burnings on the African continent, along with apparent patterns of regular monthly variability. We further discuss the characteristics and drivers behind these changes and find that biomass burning conditions in Africa may be the main factor influencing the optical properties of marine boundary aerosols.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
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Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Gaia Pinardi, Michel Van Roozendael, François Hendrick, Andreas Richter, Pieter Valks, Ramina Alwarda, Kristof Bognar, Udo Frieß, José Granville, Myojeong Gu, Paul Johnston, Cristina Prados-Roman, Richard Querel, Kimberly Strong, Thomas Wagner, Folkard Wittrock, and Margarita Yela Gonzalez
Atmos. Meas. Tech., 15, 3439–3463, https://doi.org/10.5194/amt-15-3439-2022, https://doi.org/10.5194/amt-15-3439-2022, 2022
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We report on the GOME-2A and GOME-2B OClO dataset (2007 to 2016, from the EUMETSAT's AC SAF) validation using data from nine NDACC zenith-scattered-light DOAS (ZSL-DOAS) instruments distributed in both the Arctic and Antarctic. Specific sensitivity tests are performed on the ground-based data to estimate the impact of the different OClO DOAS analysis settings and their typical errors. Good agreement is found for both the inter-annual variability and the overall OClO seasonal behavior.
Alex R. Aves, Laura E. Revell, Sally Gaw, Helena Ruffell, Alex Schuddeboom, Ngaire E. Wotherspoon, Michelle LaRue, and Adrian J. McDonald
The Cryosphere, 16, 2127–2145, https://doi.org/10.5194/tc-16-2127-2022, https://doi.org/10.5194/tc-16-2127-2022, 2022
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This study confirms the presence of microplastics in Antarctic snow, highlighting the extent of plastic pollution globally. Fresh snow was collected from Ross Island, Antarctica, and subsequent analysis identified an average of 29 microplastic particles per litre of melted snow. The most likely source of these airborne microplastics is local scientific research stations; however, modelling shows their origin could have been up to 6000 km away.
Irina Petropavlovskikh, Koji Miyagawa, Audra McClure-Beegle, Bryan Johnson, Jeannette Wild, Susan Strahan, Krzysztof Wargan, Richard Querel, Lawrence Flynn, Eric Beach, Gerard Ancellet, and Sophie Godin-Beekmann
Atmos. Meas. Tech., 15, 1849–1870, https://doi.org/10.5194/amt-15-1849-2022, https://doi.org/10.5194/amt-15-1849-2022, 2022
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The Montreal Protocol and its amendments assure the recovery of the stratospheric ozone layer that protects the Earth from harmful ultraviolet radiation. To monitor ozone recovery, multiple satellites and ground-based observational platforms collect ozone data. The changes in instruments can influence the continuation of the ozone data. We discuss a method to remove instrumental artifacts from ozone records to improve the internal consistency among multiple observational records.
Israel Silber, Robert C. Jackson, Ann M. Fridlind, Andrew S. Ackerman, Scott Collis, Johannes Verlinde, and Jiachen Ding
Geosci. Model Dev., 15, 901–927, https://doi.org/10.5194/gmd-15-901-2022, https://doi.org/10.5194/gmd-15-901-2022, 2022
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The Earth Model Column Collaboratory (EMC2) is an open-source ground-based (and air- or space-borne) lidar and radar simulator and subcolumn generator designed for large-scale models, in particular climate models, applicable also for high-resolution models. EMC2 emulates measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. We demonstrate the use of EMC2 to compare AWARE measurements with the NASA GISS ModelE3 and LES.
Sabrina P. Cochrane, K. Sebastian Schmidt, Hong Chen, Peter Pilewskie, Scott Kittelman, Jens Redemann, Samuel LeBlanc, Kristina Pistone, Michal Segal Rozenhaimer, Meloë Kacenelenbogen, Yohei Shinozuka, Connor Flynn, Rich Ferrare, Sharon Burton, Chris Hostetler, Marc Mallet, and Paquita Zuidema
Atmos. Meas. Tech., 15, 61–77, https://doi.org/10.5194/amt-15-61-2022, https://doi.org/10.5194/amt-15-61-2022, 2022
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This work presents heating rates derived from aircraft observations from the 2016 and 2017 field campaigns of ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS). We separate the total heating rates into aerosol and gas (primarily water vapor) absorption and explore some of the co-variability of heating rate profiles and their primary drivers, leading to the development of a new concept: the heating rate efficiency (HRE; the heating rate per unit aerosol extinction).
Nora Mettig, Mark Weber, Alexei Rozanov, Carlo Arosio, John P. Burrows, Pepijn Veefkind, Anne M. Thompson, Richard Querel, Thierry Leblanc, Sophie Godin-Beekmann, Rigel Kivi, and Matthew B. Tully
Atmos. Meas. Tech., 14, 6057–6082, https://doi.org/10.5194/amt-14-6057-2021, https://doi.org/10.5194/amt-14-6057-2021, 2021
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TROPOMI is a nadir-viewing satellite that has observed global atmospheric trace gases at unprecedented spatial resolution since 2017. The retrieval of ozone profiles with high accuracy has been demonstrated using the TOPAS (Tikhonov regularised Ozone Profile retrievAl with SCIATRAN) algorithm and applying appropriate spectral corrections to TROPOMI UV data. Ozone profiles from TROPOMI were compared to ozonesonde and lidar profiles, showing an agreement to within 5 % in the stratosphere.
Ruhi S. Humphries, Melita D. Keywood, Sean Gribben, Ian M. McRobert, Jason P. Ward, Paul Selleck, Sally Taylor, James Harnwell, Connor Flynn, Gourihar R. Kulkarni, Gerald G. Mace, Alain Protat, Simon P. Alexander, and Greg McFarquhar
Atmos. Chem. Phys., 21, 12757–12782, https://doi.org/10.5194/acp-21-12757-2021, https://doi.org/10.5194/acp-21-12757-2021, 2021
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The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. In this paper we present a statistical summary of the latitudinal gradient of aerosol and cloud condensation nuclei concentrations obtained from five voyages spanning the Southern Ocean.
Konstantin Baibakov, Samuel LeBlanc, Keyvan Ranjbar, Norman T. O'Neill, Mengistu Wolde, Jens Redemann, Kristina Pistone, Shao-Meng Li, John Liggio, Katherine Hayden, Tak W. Chan, Michael J. Wheeler, Leonid Nichman, Connor Flynn, and Roy Johnson
Atmos. Chem. Phys., 21, 10671–10687, https://doi.org/10.5194/acp-21-10671-2021, https://doi.org/10.5194/acp-21-10671-2021, 2021
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We find that the airborne measurements of the vertical extinction due to aerosols (aerosol optical depth, AOD) obtained in the Athabasca Oil Sands Region (AOSR) can significantly exceed ground-based values. This can have an effect on estimating the AOSR radiative impact and is relevant to satellite validation based on ground-based measurements. We also show that the AOD can marginally increase as the plumes are being transported away from the source and the new particles are being formed.
Stefanie Kremser, Mike Harvey, Peter Kuma, Sean Hartery, Alexia Saint-Macary, John McGregor, Alex Schuddeboom, Marc von Hobe, Sinikka T. Lennartz, Alex Geddes, Richard Querel, Adrian McDonald, Maija Peltola, Karine Sellegri, Israel Silber, Cliff S. Law, Connor J. Flynn, Andrew Marriner, Thomas C. J. Hill, Paul J. DeMott, Carson C. Hume, Graeme Plank, Geoffrey Graham, and Simon Parsons
Earth Syst. Sci. Data, 13, 3115–3153, https://doi.org/10.5194/essd-13-3115-2021, https://doi.org/10.5194/essd-13-3115-2021, 2021
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Aerosol–cloud interactions over the Southern Ocean are poorly understood and remain a major source of uncertainty in climate models. This study presents ship-borne measurements, collected during a 6-week voyage into the Southern Ocean in 2018, that are an important supplement to satellite-based measurements. For example, these measurements include data on low-level clouds and aerosol composition in the marine boundary layer, which can be used in climate model evaluation efforts.
Kristina Pistone, Paquita Zuidema, Robert Wood, Michael Diamond, Arlindo M. da Silva, Gonzalo Ferrada, Pablo E. Saide, Rei Ueyama, Ju-Mee Ryoo, Leonhard Pfister, James Podolske, David Noone, Ryan Bennett, Eric Stith, Gregory Carmichael, Jens Redemann, Connor Flynn, Samuel LeBlanc, Michal Segal-Rozenhaimer, and Yohei Shinozuka
Atmos. Chem. Phys., 21, 9643–9668, https://doi.org/10.5194/acp-21-9643-2021, https://doi.org/10.5194/acp-21-9643-2021, 2021
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Using aircraft-based measurements off the Atlantic coast of Africa, we found the springtime smoke plume was strongly correlated with the amount of water vapor in the atmosphere (more smoke indicated more humidity). We see the same general feature in satellite-assimilated and free-running models. Our analysis suggests this relationship is not caused by the burning but originates due to coincident continental meteorology plus fires. This air is transported over the ocean without further mixing.
Vidya Varma, Olaf Morgenstern, Kalli Furtado, Paul Field, and Jonny Williams
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-438, https://doi.org/10.5194/acp-2021-438, 2021
Revised manuscript not accepted
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We introduce a simple parametrisation whereby the immersion freezing temperature in the model is linked to the mineral dust distribution through a diagnostic function, thus invoking regional differences in the nucleation temperatures instead of the global default value of −10 °C. This provides a functionality to mimic the role of Ice Nucleating Particles in the atmosphere on influencing the short-wave radiation over the Southern Ocean region by impacting the cloud phase.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
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MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
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Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Israel Silber, Ann M. Fridlind, Johannes Verlinde, Andrew S. Ackerman, Grégory V. Cesana, and Daniel A. Knopf
Atmos. Chem. Phys., 21, 3949–3971, https://doi.org/10.5194/acp-21-3949-2021, https://doi.org/10.5194/acp-21-3949-2021, 2021
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Long-term ground-based radar and sounding measurements over Alaska (Antarctica) indicate that more than 85 % (75 %) of supercooled clouds are precipitating at cloud base and that 75 % (50 %) are precipitating to the surface. Such high prevalence is reconciled with lesser spaceborne estimates by considering radar sensitivity. Results provide a strong observational constraint for polar cloud processes in large-scale models.
Chaim I. Garfinkel, Ohad Harari, Shlomi Ziskin Ziv, Jian Rao, Olaf Morgenstern, Guang Zeng, Simone Tilmes, Douglas Kinnison, Fiona M. O'Connor, Neal Butchart, Makoto Deushi, Patrick Jöckel, Andrea Pozzer, and Sean Davis
Atmos. Chem. Phys., 21, 3725–3740, https://doi.org/10.5194/acp-21-3725-2021, https://doi.org/10.5194/acp-21-3725-2021, 2021
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Water vapor is the dominant greenhouse gas in the atmosphere, and El Niño is the dominant mode of variability in the ocean–atmosphere system. The connection between El Niño and water vapor above ~ 17 km is unclear, with single-model studies reaching a range of conclusions. This study examines this connection in 12 different models. While there are substantial differences among the models, all models appear to capture the fundamental physical processes correctly.
Jens Redemann, Robert Wood, Paquita Zuidema, Sarah J. Doherty, Bernadette Luna, Samuel E. LeBlanc, Michael S. Diamond, Yohei Shinozuka, Ian Y. Chang, Rei Ueyama, Leonhard Pfister, Ju-Mee Ryoo, Amie N. Dobracki, Arlindo M. da Silva, Karla M. Longo, Meloë S. Kacenelenbogen, Connor J. Flynn, Kristina Pistone, Nichola M. Knox, Stuart J. Piketh, James M. Haywood, Paola Formenti, Marc Mallet, Philip Stier, Andrew S. Ackerman, Susanne E. Bauer, Ann M. Fridlind, Gregory R. Carmichael, Pablo E. Saide, Gonzalo A. Ferrada, Steven G. Howell, Steffen Freitag, Brian Cairns, Brent N. Holben, Kirk D. Knobelspiesse, Simone Tanelli, Tristan S. L'Ecuyer, Andrew M. Dzambo, Ousmane O. Sy, Greg M. McFarquhar, Michael R. Poellot, Siddhant Gupta, Joseph R. O'Brien, Athanasios Nenes, Mary Kacarab, Jenny P. S. Wong, Jennifer D. Small-Griswold, Kenneth L. Thornhill, David Noone, James R. Podolske, K. Sebastian Schmidt, Peter Pilewskie, Hong Chen, Sabrina P. Cochrane, Arthur J. Sedlacek, Timothy J. Lang, Eric Stith, Michal Segal-Rozenhaimer, Richard A. Ferrare, Sharon P. Burton, Chris A. Hostetler, David J. Diner, Felix C. Seidel, Steven E. Platnick, Jeffrey S. Myers, Kerry G. Meyer, Douglas A. Spangenberg, Hal Maring, and Lan Gao
Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, https://doi.org/10.5194/acp-21-1507-2021, 2021
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Southern Africa produces significant biomass burning emissions whose impacts on regional and global climate are poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA investigation designed to study the key processes that determine these climate impacts. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project, the dataset it produced, and the most important initial findings.
Fiona M. O'Connor, N. Luke Abraham, Mohit Dalvi, Gerd A. Folberth, Paul T. Griffiths, Catherine Hardacre, Ben T. Johnson, Ron Kahana, James Keeble, Byeonghyeon Kim, Olaf Morgenstern, Jane P. Mulcahy, Mark Richardson, Eddy Robertson, Jeongbyn Seo, Sungbo Shim, João C. Teixeira, Steven T. Turnock, Jonny Williams, Andrew J. Wiltshire, Stephanie Woodward, and Guang Zeng
Atmos. Chem. Phys., 21, 1211–1243, https://doi.org/10.5194/acp-21-1211-2021, https://doi.org/10.5194/acp-21-1211-2021, 2021
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This paper calculates how changes in emissions and/or concentrations of different atmospheric constituents since the pre-industrial era have altered the Earth's energy budget at the present day using a metric called effective radiative forcing. The impact of land use change is also assessed. We find that individual contributions do not add linearly, and different Earth system interactions can affect the magnitude of the calculated effective radiative forcing.
Sabrina P. Cochrane, K. Sebastian Schmidt, Hong Chen, Peter Pilewskie, Scott Kittelman, Jens Redemann, Samuel LeBlanc, Kristina Pistone, Meloë Kacenelenbogen, Michal Segal Rozenhaimer, Yohei Shinozuka, Connor Flynn, Amie Dobracki, Paquita Zuidema, Steven Howell, Steffen Freitag, and Sarah Doherty
Atmos. Meas. Tech., 14, 567–593, https://doi.org/10.5194/amt-14-567-2021, https://doi.org/10.5194/amt-14-567-2021, 2021
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Based on observations from the 2016 and 2017 field campaigns of ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), this work establishes an observationally driven link from mid-visible aerosol optical depth (AOD) and other scene parameters to broadband shortwave irradiance (and by extension the direct aerosol radiative effect, DARE). The majority of the case-to-case DARE variability within the ORACLES dataset is attributable to the dependence on AOD and scene albedo.
Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Jean-Christopher Lambert, Henk J. Eskes, Kai-Uwe Eichmann, Ann Mari Fjæraa, José Granville, Sander Niemeijer, Alexander Cede, Martin Tiefengraber, François Hendrick, Andrea Pazmiño, Alkiviadis Bais, Ariane Bazureau, K. Folkert Boersma, Kristof Bognar, Angelika Dehn, Sebastian Donner, Aleksandr Elokhov, Manuel Gebetsberger, Florence Goutail, Michel Grutter de la Mora, Aleksandr Gruzdev, Myrto Gratsea, Georg H. Hansen, Hitoshi Irie, Nis Jepsen, Yugo Kanaya, Dimitris Karagkiozidis, Rigel Kivi, Karin Kreher, Pieternel F. Levelt, Cheng Liu, Moritz Müller, Monica Navarro Comas, Ankie J. M. Piters, Jean-Pierre Pommereau, Thierry Portafaix, Cristina Prados-Roman, Olga Puentedura, Richard Querel, Julia Remmers, Andreas Richter, John Rimmer, Claudia Rivera Cárdenas, Lidia Saavedra de Miguel, Valery P. Sinyakov, Wolfgang Stremme, Kimberly Strong, Michel Van Roozendael, J. Pepijn Veefkind, Thomas Wagner, Folkard Wittrock, Margarita Yela González, and Claus Zehner
Atmos. Meas. Tech., 14, 481–510, https://doi.org/10.5194/amt-14-481-2021, https://doi.org/10.5194/amt-14-481-2021, 2021
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This paper reports on the ground-based validation of the NO2 data produced operationally by the TROPOMI instrument on board the Sentinel-5 Precursor satellite. Tropospheric, stratospheric, and total NO2 columns are compared to measurements collected from MAX-DOAS, ZSL-DOAS, and PGN/Pandora instruments respectively. The products are found to satisfy mission requirements in general, though negative mean differences are found at sites with high pollution levels. Potential causes are discussed.
Robert G. Ryan, Jeremy D. Silver, Richard Querel, Dan Smale, Steve Rhodes, Matt Tully, Nicholas Jones, and Robyn Schofield
Atmos. Meas. Tech., 13, 6501–6519, https://doi.org/10.5194/amt-13-6501-2020, https://doi.org/10.5194/amt-13-6501-2020, 2020
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Models have identified Australasia as a formaldehyde (HCHO) hotspot from vegetation sources, but few measurement studies exist to verify this. We compare, and find good agreement between, HCHO measurements using three – two ground-based and one satellite-based – different spectroscopic techniques in Australia and New Zealand. This gives confidence in using satellite observations to study HCHO and associated air chemistry and pollution problems in this under-studied part of the world.
Yang Wang, Arnoud Apituley, Alkiviadis Bais, Steffen Beirle, Nuria Benavent, Alexander Borovski, Ilya Bruchkouski, Ka Lok Chan, Sebastian Donner, Theano Drosoglou, Henning Finkenzeller, Martina M. Friedrich, Udo Frieß, David Garcia-Nieto, Laura Gómez-Martín, François Hendrick, Andreas Hilboll, Junli Jin, Paul Johnston, Theodore K. Koenig, Karin Kreher, Vinod Kumar, Aleksandra Kyuberis, Johannes Lampel, Cheng Liu, Haoran Liu, Jianzhong Ma, Oleg L. Polyansky, Oleg Postylyakov, Richard Querel, Alfonso Saiz-Lopez, Stefan Schmitt, Xin Tian, Jan-Lukas Tirpitz, Michel Van Roozendael, Rainer Volkamer, Zhuoru Wang, Pinhua Xie, Chengzhi Xing, Jin Xu, Margarita Yela, Chengxin Zhang, and Thomas Wagner
Atmos. Meas. Tech., 13, 5087–5116, https://doi.org/10.5194/amt-13-5087-2020, https://doi.org/10.5194/amt-13-5087-2020, 2020
Ilann Bourgeois, Jeff Peischl, Chelsea R. Thompson, Kenneth C. Aikin, Teresa Campos, Hannah Clark, Róisín Commane, Bruce Daube, Glenn W. Diskin, James W. Elkins, Ru-Shan Gao, Audrey Gaudel, Eric J. Hintsa, Bryan J. Johnson, Rigel Kivi, Kathryn McKain, Fred L. Moore, David D. Parrish, Richard Querel, Eric Ray, Ricardo Sánchez, Colm Sweeney, David W. Tarasick, Anne M. Thompson, Valérie Thouret, Jacquelyn C. Witte, Steve C. Wofsy, and Thomas B. Ryerson
Atmos. Chem. Phys., 20, 10611–10635, https://doi.org/10.5194/acp-20-10611-2020, https://doi.org/10.5194/acp-20-10611-2020, 2020
Cited articles
Baars, H., Kanitz, T., Engelmann, R., Althausen, D., Heese, B., Komppula, M., Preißler, J., Tesche, M., Ansmann, A., Wandinger, U., Lim, J.-H., Ahn, J. Y., Stachlewska, I. S., Amiridis, V., Marinou, E., Seifert, P., Hofer, J., Skupin, A., Schneider, F., Bohlmann, S., Foth, A., Bley, S., Pfüller, A., Giannakaki, E., Lihavainen, H., Viisanen, Y., Hooda, R. K., Pereira, S. N., Bortoli, D., Wagner, F., Mattis, I., Janicka, L., Markowicz, K. M., Achtert, P., Artaxo, P., Pauliquevis, T., Souza, R. A. F., Sharma, V. P., van Zyl, P. G., Beukes, J. P., Sun, J., Rohwer, E. G., Deng, R., Mamouri, R.-E., and Zamorano, F.: An overview of the first decade of PollyNET: an emerging network of automated Raman-polarization lidars for continuous aerosol profiling, Atmos. Chem. Phys., 16, 5111–5137, https://doi.org/10.5194/acp-16-5111-2016, 2016. a
Baran, A. J.: A review of the light scattering properties of cirrus, J.
Quant. Spectrosc. Ra., 110, 1239–1260,
https://doi.org/10.1016/j.jqsrt.2009.02.026, 2009. a
Bastin, S., Chiriaco, M., and Drobinski, P.: Control of radiation and
evaporation on temperature variability in a WRF regional climate simulation:
comparison with colocated long term ground based observations near Paris,
Clim. Dynam., 51, 985–1003, https://doi.org/10.1007/s00382-016-2974-1, 2018. a
Bi, L., Yang, P., Kattawar, G. W., Baum, B. A., Hu, Y. X., Winker, D. M.,
Brock, R. S., and Lu, J. Q.: Simulation of the color ratio associated with
the backscattering of radiation by ice particles at the wavelengths of 0.532
and 1.064 µm, J. Geophys. Res., 114, D00H08,
https://doi.org/10.1029/2009jd011759, 2009. a
Bodas-Salcedo, A., Webb, M., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S.,
Zhang, Y., Marchand, R., Haynes, J., Pincus, R., and John, V. O.: COSP: Satellite
simulation software for model assessment, B. Am.
Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Borovoi, A., Konoshonkin, A., and Kustova, N.: Backscatter ratios for arbitrary
oriented hexagonal ice crystals of cirrus clouds, Opt. Lett., 39, 5788–5791,
https://doi.org/10.1364/OL.39.005788, 2014. a
Bouniol, D., Protat, A., Delanoë, J., Pelon, J., Piriou, J.-M., Bouyssel,
F., Tompkins, A. M., Wilson, D. R., Morille, Y., Haeffelin, M., O’Connor, E. J., Hogan, R. J., Illingworth, A. J., Donovan, D. P., and Baltink, H.: Using
continuous ground-based radar and lidar measurements for evaluating the
representation of clouds in four operational models, J. Appl.
Meteorol. Climatol., 49, 1971–1991, https://doi.org/10.1175/2010JAMC2333.1,
2010. a
Bréon, F.-M. and Doutriaux-Boucher, M.: A comparison of cloud droplet radii
measured from space, IEEE T. Geosci. Remote, 43,
1796–1805, https://doi.org/10.1109/TGRS.2005.852838, 2005. a, b
Bréon, F.-M. and Colzy, S.: Global distribution of cloud droplet effective
radius from POLDER polarization measurements, Geophys. Res. Lett.,
27, 4065–4068, https://doi.org/10.1029/2000GL011691, 2000. a
Campbell, J. R., Hlavka, D. L., Welton, E. J., Flynn, C. J., Turner, D. D.,
Spinhirne, J. D., Scott III, V. S., and Hwang, I.: Full-time, eye-safe cloud
and aerosol lidar observation at atmospheric radiation measurement program
sites: Instruments and data processing, J. Atmos. Ocean.
Tech., 19, 431–442,
https://doi.org/10.1175/1520-0426(2002)019<0431:FTESCA>2.0.CO;2, 2002. a, b
Cazorla, A., Casquero-Vera, J. A., Román, R., Guerrero-Rascado, J. L., Toledano, C., Cachorro, V. E., Orza, J. A. G., Cancillo, M. L., Serrano, A., Titos, G., Pandolfi, M., Alastuey, A., Hanrieder, N., and Alados-Arboledas, L.: Near-real-time processing of a ceilometer network assisted with sun-photometer data: monitoring a dust outbreak over the Iberian Peninsula, Atmos. Chem. Phys., 17, 11861–11876, https://doi.org/10.5194/acp-17-11861-2017, 2017. a
Chan, K. L., Wiegner, M., Flentje, H., Mattis, I., Wagner, F., Gasteiger, J., and Geiß, A.: Evaluation of ECMWF-IFS (version 41R1) operational model forecasts of aerosol transport by using ceilometer network measurements, Geosci. Model Dev., 11, 3807–3831, https://doi.org/10.5194/gmd-11-3807-2018, 2018. a
Chang, F. and Li, Z.: The effect of droplet size distribution on the
determination of cloud droplet effective radius, in: 11th ARM Science Team
Meeting, Atlanta, Ga, 19–23, 2001. a
Chepfer, H., Chiriaco, M., Vautard, R., and Spinhirne, J.: Evaluation of MM5
optically thin clouds over Europe in fall using ICESat lidar spaceborne
observations, Mon. Weather Rev., 135, 2737–2753,
https://doi.org/10.1175/MWR3413.1, 2007. a, b, c
Chepfer, H., Bony, S., Winker, D., Chiriaco, M., Dufresne, J.-L., and Sèze,
G.: Use of CALIPSO lidar observations to evaluate the cloudiness simulated by
a climate model, Geophys. Res. Lett., 35, L15704,
https://doi.org/10.1029/2008GL034207, 2008. a, b, c, d
Chiriaco, M., Vautard, R., Chepfer, H., Haeffelin, M., Dudhia, J., Wanherdrick,
Y., Morille, Y., and Protat, A.: The ability of MM5 to simulate ice clouds:
Systematic comparison between simulated and measured fluxes and lidar/radar
profiles at the SIRTA atmospheric observatory, Mon. Weather Rev., 134,
897–918, https://doi.org/10.1175/MWR3102.1, 2006. a, b, c, d, e, f, g
Chiriaco, M., Dupont, J.-C., Bastin, S., Badosa, J., Lopez, J., Haeffelin, M., Chepfer, H., and Guzman, R.: ReOBS: a new approach to synthesize long-term multi-variable dataset and application to the SIRTA supersite, Earth Syst. Sci. Data, 10, 919–940, https://doi.org/10.5194/essd-10-919-2018, 2018. a, b
Costa-Surós, M., Calbó, J., González, J., and Martin-Vide, J.: Behavior of
cloud base height from ceilometer measurements, Atmos. Res., 127,
64–76, https://doi.org/10.1016/j.atmosres.2013.02.005, 2013. a
Cromwell, E. and Flynn, D.: Lidar Cloud Detection With Fully Convolutional Networks, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 619–627, https://doi.org/10.1109/WACV.2019.00071, 2019. a
Cromwell, E. and Flynn, D.: Lidar cloud detection with fully convolutional
networks, in: 2019 IEEE Winter Conference on Applications of Computer Vision
(WACV), 619–627, IEEE, 2019. a
Dee, D. P., Uppala, S. M., Simmons, A., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M., Balsamo, G., Bauer, d. P., et al.: The
ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E.,
Kahn, R. A., Martonchik, J. V., Ackerman, T. P., Davies, R., Gerstl, S. A. W., Gordon, H. R., Muller, J.. Myneni, R. B., Sellers, P. J., Pinty, B., and Verstraete, M. M.: Multi-angle Imaging SpectroRadiometer (MISR) instrument description
and experiment overview, IEEE T. Geosci. Remote,
36, 1072–1087, https://doi.org/10.1109/36.700992, 1998. a
Dionisi, D., Barnaba, F., Diémoz, H., Di Liberto, L., and Gobbi, G. P.: A multiwavelength numerical model in support of quantitative retrievals of aerosol properties from automated lidar ceilometers and test applications for AOT and PM10 estimation, Atmos. Meas. Tech., 11, 6013–6042, https://doi.org/10.5194/amt-11-6013-2018, 2018. a
Ebita, A., Kobayashi, S., Ota, Y., Moriya, M., Kumabe, R., Onogi, K., Harada,
Y., Yasui, S., Miyaoka, K., Takahashi, K., Kamahori, H., Kobayashi, C., Endo, H., Soma, M., Oikawa, Y., and Ishimizu, T.: The Japanese 55-year
reanalysis “JRA-55”: an interim report, Sola, 7, 149–152,
https://doi.org/10.2151/jmsj.2016-015, 2011. a
ECMWF: Copernicus Climate Change Service (C3S) (2017): ERA5: Fifth generation
of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate
Change Service Climate Data Store (CDS), https://doi.org/10.24381/cds.bd0915c6, available at:
https://cds.climate.copernicus.eu/cdsapp#!/home (last access: 1 January 2021), 2019. a
Edwards, J. and Slingo, A.: Studies with a flexible new radiation code. I:
Choosing a configuration for a large-scale model, Q. J.
Roy. Meteorol. Soc., 122, 689–719, https://doi.org/10.1002/qj.49712253107,
1996. a
Emeis, S.: Surface-based remote sensing of the atmospheric boundary layer,
vol. 40, Springer Science & Business Media, https://doi.org/10.1007/978-90-481-9340-0,
2010. a, b, c
Emeis, S., Schäfer, K., and Münkel, C.: Observation of the structure of
the urban boundary layer with different ceilometers and validation by RASS
data, Meteorol. Z., 18, 149–154,
https://doi.org/10.1127/0941-2948/2009/0365, 2009. a
Eresmaa, N., Karppinen, A., Joffre, S. M., Räsänen, J., and Talvitie, H.: Mixing height determination by ceilometer, Atmos. Chem. Phys., 6, 1485–1493, https://doi.org/10.5194/acp-6-1485-2006, 2006. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G.,
Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M.,
Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P., Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A. G., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D., Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.: Taking climate model evaluation to the next level, Nat. Clim.
Change, 9, 102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019. a
Flynn, C. J., Mendozaa, A., Zhengb, Y., and Mathurb, S.: Novel
polarization-sensitive micropulse lidar measurement technique, Opt.
Exp., 15, 2785–2790, https://doi.org/10.1364/OE.15.002785, 2007. a
Fu, D., Di Girolamo, L., Liang, L., and Zhao, G.: Regional Biases in MODIS
Marine Liquid Water Cloud Drop Effective Radius Deduced Through Fusion With
MISR, J. Geophys. Res.-Atmos., 124, 13182–13196,
https://doi.org/10.1029/2019JD031063, 2019. a
Garnier, A., Pelon, J., Vaughan, M. A., Winker, D. M., Trepte, C. R., and Dubuisson, P.: Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans, Atmos. Meas. Tech., 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, 2015. a, b, c, d, e, f, g
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., 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
Geleyn, J. and Hollingsworth, A.: An economical analytical method for the
computation of the interaction between scattering and line absorption of
radiation, Contributions to Atmospheric Physics, 52, 1–16, 1979. a
Goody, R. M. and Yung, Y. L.: Atmospheric radiation: theoretical basis, Oxford
University Press, New York, NY, USA, 2 edn., 1995. a
Hansen, A., Ament, F., Grützun, V., and Lammert, A.: Model evaluation by a cloud classification based on multi-sensor observations, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-259, 2018a. a
Hansen, A., Ament, F., Grützun, V., and Lammert, A.: Model evaluation by a cloud classification based on multi-sensor observations, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-259, 2018b. a
Harada, Y., Kamahori, H., Kobayashi, C., Endo, H., Kobayashi, S., Ota, Y.,
Onoda, H., Onogi, K., Miyaoka, K., and Takahashi, K.: The JRA-55 Reanalysis:
Representation of atmospheric circulation and climate variability, J. Meteorol. Soc. Japan. Ser. II, 94, 269–302,
https://doi.org/10.2151/jmsj.2016-015, 2016. a
Heese, B., Flentje, H., Althausen, D., Ansmann, A., and Frey, S.: Ceilometer lidar comparison: backscatter coefficient retrieval and signal-to-noise ratio determination, Atmos. Meas. Tech., 3, 1763–1770, https://doi.org/10.5194/amt-3-1763-2010, 2010. a
Heymsfield, A. J.: Extinction-ice water content-effective radius algorithms for
CALIPSO, Geophys. Res. Lett., 32, L10807, https://doi.org/10.1029/2005gl022742,
2005. a, b, c, d
Hines, K. M. and Bromwich, D. H.: Development and testing of Polar Weather
Research and Forecasting (WRF) model. Part I: Greenland ice sheet
meteorology, Mon. Weather Rev., 136, 1971–1989,
https://doi.org/10.1175/2007MWR2112.1, 2008. a
Hogan, R. J.: Fast approximate calculation of multiply scattered lidar returns,
Appl. Opt., 45, 5984–5992, https://doi.org/10.1364/AO.45.005984, 2006. a, b, c
Hogan, R. J., Jakob, C., and Illingworth, A. J.: Comparison of ECMWF
Winter-Season Cloud Fraction with Radar-Derived Values, J. Appl.
Meteorol., 40, 513–525,
https://doi.org/10.1175/1520-0450(2001)040<0513:COEWSC>2.0.CO;2, 2001. a
Hopkin, E.: Use of a calibrated ceilometer network to improve high resolution
weather forecasts, Ph.D. thesis, University of Reading, UK, 2018. a
Hopkin, E., Illingworth, A. J., Charlton-Perez, C., Westbrook, C. D., and Ballard, S.: A robust automated technique for operational calibration of ceilometers using the integrated backscatter from totally attenuating liquid clouds, Atmos. Meas. Tech., 12, 4131–4147, https://doi.org/10.5194/amt-12-4131-2019, 2019. a, b, c, d, e, f, g, h, i, j, k, l
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q.,
Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., and Williamson, D. : The art and science of
climate model tuning, B. Am. Meteorol. Soc., 98,
589–602, https://doi.org/10.1175/BAMS-D-15-00135.1, 2017. a
Hu, Y.: Depolarization ratio–effective lidar ratio relation: Theoretical
basis for space lidar cloud phase discrimination, Geophys. Res.
Lett., 34, L11812, https://doi.org/10.1029/2007GL029584, 2007. a
Hu, Y., Vaughan, M., McClain, C., Behrenfeld, M., Maring, H., Anderson, D., Sun-Mack, S., Flittner, D., Huang, J., Wielicki, B., Minnis, P., Weimer, C., Trepte, C., and Kuehn, R.: Global statistics of liquid water content and effective number concentration of water clouds over ocean derived from combined CALIPSO and MODIS measurements, Atmos. Chem. Phys., 7, 3353–3359, https://doi.org/10.5194/acp-7-3353-2007, 2007. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci.
Eng., 9, 90, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Illingworth, A., Hogan, R., O'connor, E., Bouniol, D., Brooks, M., Delanoë,
J., Donovan, D., Eastment, J., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Baltink, H. K., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.: Cloudnet:
Continuous evaluation of cloud profiles in seven operational models using
ground-based observations, B. Am. Meteorol. Soc.,
88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007. a, b
Illingworth, A., Cimini, D., Haefele, A., Haeffelin, M., Hervo, M., Kotthaus,
S., Löhnert, U., Martinet, P., Mattis, I., O’Connor, E. J., and Potthast, R.: How
can Existing Ground-Based Profiling Instruments Improve European Weather
Forecasts?, B. Am. Meteorol. Soc., 100, 605–619,
https://doi.org/10.1175/BAMS-D-17-0231.1, 2018. a, b
Illingworth, A. J., Barker, H., Beljaars, A., Ceccaldi, M., Chepfer, H.,
Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P.,
Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A., Wandinger, U., Wehr, T., and van Zadelhoff, G.-J.: The EarthCARE satellite: The next step forward in global measurements
of clouds, aerosols, precipitation, and radiation, B. Am.
Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1,
2015a. a
Illingworth, A. J., Cimini, D., Gaffard, C., Haeffelin, M., Lehmann, V.,
Löhnert, U., O’Connor, E. J., and Ruffieux, D.: Exploiting existing
ground-based remote sensing networks to improve high-resolution weather
forecasts, B. Am. Meteorol. Soc., 96, 2107–2125,
https://doi.org/10.1175/BAMS-D-13-00283.1, 2015b. a
Jin, Y., Kai, K., Kawai, K., Nagai, T., Sakai, T., Yamazaki, A., Uchiyama, A.,
Batdorj, D., Sugimoto, N., and Nishizawa, T.: Ceilometer calibration for
retrieval of aerosol optical properties, J. Quant. Spectrosc. Ra., 153, 49–56, https://doi.org/10.1016/j.jqsrt.2014.10.009, 2015. a, b, c
Josset, D., Pelon, J., Garnier, A., Hu, Y., Vaughan, M., Zhai, P.-W., Kuehn,
R., and Lucker, P.: Cirrus optical depth and lidar ratio retrieval from
combined CALIPSO-CloudSat observations using ocean surface echo, J. Geophys. Res.-Atmos., 117, D05207,
https://doi.org/10.1029/2011jd016959, 2012. a
Klein, S. A. and Jakob, C.: Validation and sensitivities of frontal clouds
simulated by the ECMWF model, Mon. Weather Rev., 127, 2514–2531,
https://doi.org/10.1175/1520-0493(1999)127<2514:VASOFC>2.0.CO;2, 1999. a
Klekociuk, A. R., French, W. J. R., Alexander, S. P., Kuma, P., and McDonald,
A. J.: The state of the atmosphere in the 2016 southern Kerguelen Axis
campaign region, Deep Sea Res. Pt. II, 174, 0967-0645,
https://doi.org/10.1016/j.dsr2.2019.02.001, 2019. a
Knepp, T. N., Szykman, J. J., Long, R., Duvall, R. M., Krug, J., Beaver, M., Cavender, K., Kronmiller, K., Wheeler, M., Delgado, R., Hoff, R., Berkoff, T., Olson, E., Clark, R., Wolfe, D., Van Gilst, D., and Neil, D.: Assessment of mixed-layer height estimation from single-wavelength ceilometer profiles, Atmos. Meas. Tech., 10, 3963–3983, https://doi.org/10.5194/amt-10-3963-2017, 2017. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.: The JRA-55 reanalysis:
General specifications and basic characteristics, J.
Meteorol. Soc. Japan. Ser. II, 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015. a
Kotthaus, S., O'Connor, E., Münkel, C., Charlton-Perez, C., Haeffelin, M., Gabey, A. M., and Grimmond, C. S. B.: Recommendations for processing atmospheric attenuated backscatter profiles from Vaisala CL31 ceilometers, Atmos. Meas. Tech., 9, 3769–3791, https://doi.org/10.5194/amt-9-3769-2016, 2016. a, b, c, d
Kuma, P.: cl2nc 3.3.0, Zenodo, https://doi.org/10.5281/zenodo.4409716, 2020a. a
Kuma, P.: mpl2nc 1.3.5, Zenodo, https://doi.org/10.5281/zenodo.4409731, 2020b. a
Kuma, P., McDonald, A. J., Morgenstern, O., Alexander, S. P., Cassano, J. J., Garrett, S., Halla, J., Hartery, S., Harvey, M. J., Parsons, S., Plank, G., Varma, V., and Williams, J.: Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations, Atmos. Chem. Phys., 20, 6607–6630, https://doi.org/10.5194/acp-20-6607-2020, 2020. a, b, c
Kuma, P., McDonald, A. J., Morgenstern, O., Querel, R., Silber, I., and Flynn, C. J.: Automatic Lidar and Ceilometer Framework (ALCF) (Version 1.0.0), Zenodo, https://doi.org/10.5281/zenodo.4411633, 2021. a
Lamer, K., Fridlind, A. M., Ackerman, A. S., Kollias, P., Clothiaux, E. E., and Kelley, M.: (GO)2-SIM: a GCM-oriented ground-observation forward-simulator framework for objective evaluation of cloud and precipitation phase, Geosci. Model Dev., 11, 4195–4214, https://doi.org/10.5194/gmd-11-4195-2018, 2018. a
Lewis, J. R., Campbell, J. R., Welton, E. J., Stewart, S. A., and Haftings,
P. C.: Overview of MPLNET version 3 cloud detection, J. Atmos. Ocean. Tech., 33, 2113–2134, https://doi.org/10.1175/JTECH-D-15-0190.1,
2016. a
Liou, K.-N.: An introduction to atmospheric radiation, vol. 84, Elsevier, 2
edn., 2002. a
Liu, J., Li, Z., Zheng, Y., and Cribb, M.: Cloud-base distribution and cirrus
properties based on micropulse lidar measurements at a site in southeastern
China, Adv. Atmos. Sci., 32, 991–1004,
https://doi.org/10.1007/s00376-014-4176-2, 2015a. a
Liu, L., Sun, X.-J., Liu, X.-C., Gao, T.-C., and Zhao, S.-J.: Comparison of
cloud base height derived from a ground-based infrared cloud measurement and
two ceilometers, Adv. Meteorol., 2015, 1687-9309, https://doi.org/10.1155/2015/853861,
2015b. a, b
Madonna, F., Rosoldi, M., Lolli, S., Amato, F., Vande Hey, J., Dhillon, R., Zheng, Y., Brettle, M., and Pappalardo, G.: Intercomparison of aerosol measurements performed with multi-wavelength Raman lidars, automatic lidars and ceilometers in the framework of INTERACT-II campaign, Atmos. Meas. Tech., 11, 2459–2475, https://doi.org/10.5194/amt-11-2459-2018, 2018. a, b
Marenco, F., Santacesaria, V., Bais, A. F., Balis, D., di Sarra, A.,
Papayannis, A., and Zerefos, C.: Optical properties of tropospheric aerosols
determined by lidar and spectrophotometric measurement (Photochemical
Activity and Solar Ultraviolet Radiation campaign), Appl. Opt., 36,
6875–6886, https://doi.org/10.1364/AO.36.006875, 1997. a
Martucci, G., Milroy, C., and O’Dowd, C. D.: Detection of cloud-base height
using Jenoptik CHM15K and Vaisala CL31 ceilometers, J. Atmos. Ocean. Tech., 27, 305–318, https://doi.org/10.1175/2009JTECHA1326.1, 2010. a, b
Masunaga, H., Matsui, T., Tao, W.-k., Hou, A. Y., Kummerow, C. D., Nakajima,
T., Bauer, P., Olson, W. S., Sekiguchi, M., and Nakajima, T. Y.: Satellite
data simulator unit: A multisensor, multispectral satellite simulator
package, B. Am. Meteorol. Soc., 91, 1625–1632,
https://doi.org/10.1175/2010BAMS2809.1, 2010. a
Matsui, T.: Goddard Satellite Data Simulator Unit (G-SDSU), https://cloud.gsfc.nasa.gov/index.php?section=14 (last access: January 2021),
2019. a
Mattis, I., Begbie, R., Boyouk, N., Bravo-Aranda, J. A., Brettle, M.,
Cermak, J., Drouin, M.-A., Geiß, A., Görsdorf, U., Haefele,
A., Haeffelin, M., Hervo, M., Komínková, K., Leinweber, R.,
Müller, G., Münkel, C., Pattantyús-Ábrahám, M.,
Pönitz, K., Wagner, F., and Wiegner, M.: The ceilometer
inter-comparison campaign CeiLinEx2015, in: EGU General Assembly Conference
Abstracts, EPSC2016–9687, 2016. a, b
McGill, M. J., Yorks, J. E., Scott, V. S., Kupchock, A. W., and Selmer, P. A.:
The Cloud-Aerosol Transport System (CATS): A technology demonstration on the
International Space Station, in: Lidar Remote Sensing for Environmental
Monitoring XV, vol. 9612, p. 96120A, International Society for Optics and
Photonics, 2015. a
Mie, G.: Beiträge zur Optik trüber Medien, speziell kolloidaler
Metallösungen, Annalen der Physik, 330, 377–445,
https://doi.org/10.1002/andp.19083300302, 1908. a
Milroy, C., Martucci, G., Lolli, S., Loaec, S., Sauvage, L., Xueref-Remy, I.,
Lavrič, J. V., Ciais, P., Feist, D. G., Biavati, G., and O'Dowd, C. D.: An
assessment of pseudo-operational ground-based light detection and ranging
sensors to determine the boundary-layer structure in the coastal atmosphere,
Adv. Meteorol., 2012, 929080, https://doi.org/10.1155/2012/929080, 2012. a
Morcrette, C. J., O'Connor, E. J., and Petch, J. C.: Evaluation of two cloud
parametrization schemes using ARM and Cloud-Net observations, Q.
J. Roy. Meteorol. Soc., 138, 964–979,
https://doi.org/10.1002/qj.969, 2012. a
Morille, Y., Haeffelin, M., Drobinski, P., and Pelon, J.: STRAT: An automated
algorithm to retrieve the vertical structure of the atmosphere from
single-channel lidar data, J. Atmos. Ocean. Tech.,
24, 761–775, https://doi.org/10.1175/JTECH2008.1, 2007. a, b
Münkel, C., Eresmaa, N., Räsänen, J., and Karppinen, A.: Retrieval
of mixing height and dust concentration with lidar ceilometer, Bound.-Lay.
Meteorol., 124, 117–128, https://doi.org/10.1007/s10546-006-9103-3, 2007. a
NASA JPL: NASA Shuttle Radar Topography Mission Global 3 arc second [Data
set], NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL3.003, 2013. a
Noel, V. and Chepfer, H.: A global view of horizontally oriented crystals in
ice clouds from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observation (CALIPSO), J. Geophys. Res., 115, D00H23,
https://doi.org/10.1029/2009jd012365, 2010. a
O'Connor, E. J., Illingworth, A. J., and Hogan, R. J.: A technique for
autocalibration of cloud lidar, J. Atmos. Ocean.
Tech., 21, 777–786,
https://doi.org/10.1175/1520-0426(2004)021<0777:ATFAOC>2.0.CO;2, 2004. a, b, c
Pal, S. R., Steinbrecht, W., and Carswell, A. I.: Automated method for lidar
determination of cloud-base height and vertical extent, Appl. Opt., 31,
1488–1494, https://doi.org/10.1364/AO.31.001488, 1992. a
Parkinson, C. L.: Aqua: An Earth-observing satellite mission to examine water
and other climate variables, IEEE T. Geosci. Remote, 41, 173–183, https://doi.org/10.1109/TGRS.2002.808319, 2003. a
Petty, G. W. and Huang, W.: The modified gamma size distribution applied to
inhomogeneous and nonspherical particles: Key relationships and conversions,
J. Atmos. Sci., 68, 1460–1473,
https://doi.org/10.1175/2011JAS3645.1, 2011. a, b, c
Powers, J. G., Monaghan, A. J., Cayette, A. M., Bromwich, D. H., Kuo, Y.-H.,
and Manning, K. W.: Real-Time Mesoscale Modeling Over Antarctica: The
Antarctic Mesoscale Prediction System, B. Am.
Meteorol. Soc., 84, 1533–1546, https://doi.org/10.1175/BAMS-84-11-1533, 2003. a
Price-Whelan, A. M., Sipőcz, B. M., Günther, H. M., et al.: The
Astropy Project: Building an Open-science Project and Status of the v2.0 Core
Package, Astronomical J., 156, 123, https://doi.org/10.3847/1538-3881/aabc4f,
2018. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, available at:
https://www.R-project.org/ (last access: 1 January 2021), 2017. a
Rausch, J., Meyer, K., Bennartz, R., and Platnick, S.: Differences in liquid cloud droplet effective radius and number concentration estimates between MODIS collections 5.1 and 6 over global oceans, Atmos. Meas. Tech., 10, 2105–2116, https://doi.org/10.5194/amt-10-2105-2017, 2017. a
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V.,
Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface
temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res.-Atmos., 108, 4407,
https://doi.org/10.1029/2002JD002670, 2003. a
Rew, R. and Davis, G.: NetCDF: an interface for scientific data access, IEEE
Comput. Graph. Appl., 10, 76–82, https://doi.org/10.1109/38.56302, 1990. a
Rosoldi, M., Madonna, F., Pappalardo, G., Hey, J. V., and Zheng, Y.: The lesson
learnt during interact-I and INTERACT-II actris measurement campaigns, in:
EPJ Web of Conferences, vol. 176, p. 11002, EDP Sciences, 2018. a
Rossow, W. B. and Schiffer, R. A.: ISCCP cloud data products, B.
Am. Meteorol. Soc., 72, 2–20,
https://doi.org/10.1175/1520-0477(1991)072<0002:ICDP>2.0.CO;2, 1991. a
Rossum, G.: Python reference manual, Centre for Mathematics and Computer Science, Amsterdam, Netherlands, 1995. a
Schmidt, G. A., Bader, D., Donner, L. J., Elsaesser, G. S., Golaz, J.-C., Hannay, C., Molod, A., Neale, R. B., and Saha, S.: Practice and philosophy of climate model tuning across six US modeling centers, Geosci. Model Dev., 10, 3207–3223, https://doi.org/10.5194/gmd-10-3207-2017, 2017. a
Silber, I., Verlinde, J., Eloranta, E. W., Flynn, C. J., and Flynn, D. M.:
Polar liquid cloud base detection algorithms for high spectral resolution or
micropulse lidar data, J. Geophys. Res.-Atmos., 123,
4310–4322, https://doi.org/10.1029/2017JD027840, 2018. a, b, c, d
Spinhirne, J. D.: Micro pulse lidar, IEEE T. Geosci. Remote, 31, 48–55, 1993. a
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z.,
Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team:
The CloudSat mission and the A-Train: A new dimension of space-based
observations of clouds and precipitation, B. Am.
Meteorol. Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002. a
Stokes, G. M. and Schwartz, S. E.: The Atmospheric Radiation Measurement (ARM)
Program: Programmatic background and design of the cloud and radiation test
bed, B. Am. Meteorol. Soc., 75, 1201–1222,
https://doi.org/10.1175/1520-0477(1994)075<1201:TARMPP>2.0.CO;2, 1994. a
Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geosci. Model Dev., 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, 2018. a
Telford, P. J., Braesicke, P., Morgenstern, O., and Pyle, J. A.: Technical Note: Description and assessment of a nudged version of the new dynamics Unified Model, Atmos. Chem. Phys., 8, 1701–1712, https://doi.org/10.5194/acp-8-1701-2008, 2008. a
Torvalds, L.: Linux: a portable operating system, Master's thesis, University
of Helsinki, 1997. a
Tsaknakis, G., Papayannis, A., Kokkalis, P., Amiridis, V., Kambezidis, H. D., Mamouri, R. E., Georgoussis, G., and Avdikos, G.: Inter-comparison of lidar and ceilometer retrievals for aerosol and Planetary Boundary Layer profiling over Athens, Greece, Atmos. Meas. Tech., 4, 1261–1273, https://doi.org/10.5194/amt-4-1261-2011, 2011. a
Van Der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy array: a
structure for efficient numerical computation, Comput. Sci.
Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
van Diedenhoven, B.: Remote Sensing of Crystal Shapes in Ice Clouds, in:
Springer Series in Light Scattering, pp. 197–250, Springer International
Publishing, https://doi.org/10.1007/978-3-319-70808-9_5, 2017. a
Van Tricht, K., Gorodetskaya, I. V., Lhermitte, S., Turner, D. D., Schween, J. H., and Van Lipzig, N. P. M.: An improved algorithm for polar cloud-base detection by ceilometer over the ice sheets, Atmos. Meas. Tech., 7, 1153–1167, https://doi.org/10.5194/amt-7-1153-2014, 2014. a, b
Vaughan, M. A., Liu, Z., McGill, M. J., Hu, Y., and Obland, M. D.: On the
spectral dependence of backscatter from cirrus clouds: Assessing CALIOP's
1064 nm calibration assumptions using cloud physics lidar measurements,
J. Geophys. Res.-Atmos., 115, D14206,
https://doi.org/10.1029/2009JD013086, 2010. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright,
J., van der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K.,
Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E.,
Carey, C., Polat, İ., Feng, Y., Moore, E. W., Vand erPlas, J.,
Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero,
E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa,
F., van Mulbregt, P., and Contributors: SciPy 1.0–Fundamental
Algorithms for Scientific Computing in Python, arXiv [preprint], arXiv:1907.10121, 11 December 2019. a
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a
Wang, Z. and Sassen, K.: Cloud type and macrophysical property retrieval using
multiple remote sensors, J. Appl. Meteorol., 40, 1665–1682,
https://doi.org/10.1175/1520-0450(2001)040<1665:CTAMPR>2.0.CO;2, 2001. a, b
Warren, E., Charlton-Perez, C., Kotthaus, S., Lean, H., Ballard, S., Hopkin,
E., and Grimmond, S.: Evaluation of forward-modelled attenuated backscatter
using an urban ceilometer network in London under clear-sky conditions,
Atmos. Environ., 191, 532–547,
https://doi.org/10.1016/j.atmosenv.2018.04.045, 2018. a
Watson-Parris, D., Schutgens, N., Cook, N., Kipling, Z., Kershaw, P., Gryspeerdt, E., Lawrence, B., and Stier, P.: Community Intercomparison Suite (CIS) v1.4.0: a tool for intercomparing models and observations, Geosci. Model Dev., 9, 3093–3110, https://doi.org/10.5194/gmd-9-3093-2016, 2016. a
Webb, M., Senior, C., Bony, S., and Morcrette, J.-J.: Combining ERBE and ISCCP
data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate
models, Clim. Dynam., 17, 905–922, https://doi.org/10.1007/s003820100157, 2001. a
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017. a
Welton, E. J., Voss, K. J., Gordon, H. R., Maring, H., Smirnov, A., Holben, B.,
Schmid, B., Livingston, J. M., Russell, P. B., Durkee, P. A., Formenti, P., and Andreae, M. O.:
Ground-based lidar measurements of aerosols during ACE-2: Instrument
description, results, and comparisons with other ground-based and airborne
measurements, Tellus B, 52, 636–651, https://doi.org/10.1034/j.1600-0889.2000.00025.x,
2000. a
Welton, E. J., Voss, K. J., Quinn, P. K., Flatau, P. J., Markowicz, K.,
Campbell, J. R., Spinhirne, J. D., Gordon, H. R., and Johnson, J. E.:
Measurements of aerosol vertical profiles and optical properties during
INDOEX 1999 using micropulse lidars, J. Geophys. Res.-Atmos., 107, INX2–18, https://doi.org/10.1029/2000JD000038, 2002. a
Welton, E. J., Campbell, J. R., Berkoff, T. A., Valencia, S., Spinhirne, J. D.,
Holben, B., Tsay, S.-C., and Schmid, B.: The NASA Micro-Pulse Lidar Network
(MPLNET): an overview and recent results, Opt. Pur. Apl, 39, 67–74, 2006. a
Werner, M.: Shuttle radar topography mission (SRTM) mission overview,
Frequenz, 55, 75–79, https://doi.org/10.1515/FREQ.2001.55.3-4.75, 2001. a
Wiegner, M. and Gasteiger, J.: Correction of water vapor absorption for aerosol remote sensing with ceilometers, Atmos. Meas. Tech., 8, 3971–3984, https://doi.org/10.5194/amt-8-3971-2015, 2015. a, b, c, d
Wiegner, M. and Geiß, A.: Aerosol profiling with the Jenoptik ceilometer CHM15kx, Atmos. Meas. Tech., 5, 1953–1964, https://doi.org/10.5194/amt-5-1953-2012, 2012. a
Wiegner, M., Madonna, F., Binietoglou, I., Forkel, R., Gasteiger, J., Geiß, A., Pappalardo, G., Schäfer, K., and Thomas, W.: What is the benefit of ceilometers for aerosol remote sensing? An answer from EARLINET, Atmos. Meas. Tech., 7, 1979–1997, https://doi.org/10.5194/amt-7-1979-2014, 2014. a, b, c, d
Wiegner, M., Mattis, I., Pattantyús-Ábrahám, M., Bravo-Aranda, J. A., Poltera, Y., Haefele, A., Hervo, M., Görsdorf, U., Leinweber, R., Gasteiger, J., Haeffelin, M., Wagner, F., Cermak, J., Komínková, K., Brettle, M., Münkel, C., and Pönitz, K.: Aerosol backscatter profiles from ceilometers: validation of water vapor correction in the framework of CeiLinEx2015, Atmos. Meas. Tech., 12, 471–490, https://doi.org/10.5194/amt-12-471-2019, 2019. a, b
Williams, D. N., Ananthakrishnan, R., Bernholdt, D., Bharathi, S., Brown, D.,
Chen, M., Chervenak, A., Cinquini, L., Drach, R., Foster, I., et al.: The
Earth System Grid: Enabling access to multimodel climate simulation data,
B. Am. Meteorol. Soc., 90, 195–206,
https://doi.org/10.1175/2008BAMS2459.1, 2009. a
Williams, J., Morgenstern, O., Varma, V., Behrens, E., Hayek, W., Oliver, H.,
Dean, S., Mullan, B., and Frame, D.: Development of the New Zealand Earth
System Model: NZESM, Weather and Climate, 36, 25–44, https://doi.org/10.2307/26779386,
2016. a
Williams, K. D. and Bodas-Salcedo, A.: A multi-diagnostic approach to cloud evaluation, Geosci. Model Dev., 10, 2547–2566, https://doi.org/10.5194/gmd-10-2547-2017, 2017. a
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt,
W. H., and Young, S. A.: Overview of the CALIPSO mission and CALIOP data
processing algorithms, J. Atmos. Ocean. Tech., 26,
2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009. a
Wiscombe, W. J.: Mie scattering calculations: Advances in technique and fast,
vector-speed computer codes, Tech. rep., National Center for Atmospheric
Research Boulder, Colorado, 1979. a
Wiscombe, W. J.: Improved Mie scattering algorithms, Appl. Opt., 19,
1505–1509, 1980. a
Yang, P., Liou, K.-N., Bi, L., Liu, C., Yi, B., and Baum, B. A.: On the
radiative properties of ice clouds: Light scattering, remote sensing, and
radiation parameterization, Adv. Atmos. Sci., 32, 32–63,
https://doi.org/10.1007/s00376-014-0011-z, 2014. a
Yorks, J. E., Hlavka, D. L., Hart, W. D., and McGill, M. J.: Statistics of
Cloud Optical Properties from Airborne Lidar Measurements, J.
Atmos. Ocean. Tech., 28, 869–883,
https://doi.org/10.1175/2011jtecha1507.1, 2011.
a
Zadra, A., Williams, K., Frassoni, A., Rixen, M., Adames, Á. F., Berner,
J., Bouyssel, F., Casati, B., Christensen, H., Ek, M. B., Flato, G., Huang, Y., Judt, F., Lin, H., Maloney, E., Merryfield, W., Van Niekerk, A., Rackow, T., Saito, K., Wedi, N., and Yadav, P.: Systematic
Errors in Weather and Climate Models: Nature, Origins, and Ways Forward,
B. Am. Meteorol. Soc., 99, ES67–ES70,
https://doi.org/10.1175/BAMS-D-17-0287.1, 2018. a
Zdunkowski, W., Trautmann, T., and Bott, A.: Radiation in the atmosphere: a
course in theoretical meteorology, Cambridge University Press, New York, NY, USA, 482 pp., ISBN 0-511-27560-9, 2007. a
Zhang, Y., Xie, S., Klein, S. A., Marchand, R., Kollias, P., Clothiaux, E. E.,
Lin, W., Johnson, K., Swales, D., Bodas-Salcedo, A., Tang, S., Haynes, J. M., Collis, S., Jensen, M., Bharadwaj, N., Hardin, J., and Isom, B.: The ARM Cloud
Radar Simulator for Global Climate Models: Bridging Field Data and Climate
Models, B. Am. Meteorol. Soc., 99, 21–26,
https://doi.org/10.1175/BAMS-D-16-0258.1, 2018. a
Zhang, Z. and Platnick, S.: An assessment of differences between cloud
effective particle radius retrievals for marine water clouds from three MODIS
spectral bands, J. Geophys. Res.-Atmos., 116, D20215,
https://doi.org/10.1029/2011JD016216, 2011. a