Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-901-2022
© Author(s) 2022. 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-15-901-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Earth Model Column Collaboratory (EMC2) v1.1: an open-source ground-based lidar and radar instrument simulator and subcolumn generator for large-scale models
Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
Robert C. Jackson
Environmental Sciences Division, Argonne National Laboratory, Argonne, IL, USA
Ann M. Fridlind
NASA Goddard Institute for Space Studies, New York, NY, USA
Andrew S. Ackerman
NASA Goddard Institute for Space Studies, New York, NY, USA
Scott Collis
Environmental Sciences Division, Argonne National Laboratory, Argonne, IL, USA
Johannes Verlinde
Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA
Jiachen Ding
Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA
Related authors
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Richard Querel, Israel Silber, and Connor J. Flynn
Geosci. Model Dev., 14, 43–72, https://doi.org/10.5194/gmd-14-43-2021, https://doi.org/10.5194/gmd-14-43-2021, 2021
Keith M. Hines, David H. Bromwich, Sheng-Hung Wang, Israel Silber, Johannes Verlinde, and Dan Lubin
Atmos. Chem. Phys., 19, 12431–12454, https://doi.org/10.5194/acp-19-12431-2019, https://doi.org/10.5194/acp-19-12431-2019, 2019
Short summary
Short summary
We explore how well clouds are represented in numerical weather prediction over Antarctica, a very difficult environment for field programs where few studies have been conducted. Fortunately, a 2015–2017 field program for West Antarctica supplied observations. We achieve promising results with newer, more advanced cloud schemes. We need to understand the role of clouds and precipitation in the maintenance of the Antarctic ice mass to understand and predict sea level change over the 21st century.
Israel Silber, Colin Price, and Craig J. Rodger
Atmos. Chem. Phys., 16, 3279–3288, https://doi.org/10.5194/acp-16-3279-2016, https://doi.org/10.5194/acp-16-3279-2016, 2016
Short summary
Short summary
We report for the first time that the semi-annual oscillation (SAO) is one of the dominant oscillations in the nighttime lower ionosphere, using ground-based measurements of VLF signals reflected off the lower part of the ionosphere. We conclude that the origins of this oscillation are oscillatory changes of the D region's electrical characteristics, driven by NOx transport from the lower thermosphere. This oscillation should be considered in lower ionospheric and VLF wave propagation models.
Kamil Mroz, Alessandro Battaglia, and Ann M. Fridlind
EGUsphere, https://doi.org/10.5194/egusphere-2023-2117, https://doi.org/10.5194/egusphere-2023-2117, 2023
Short summary
Short summary
In this study, we examine the extent to which radar measurements from space can inform us about the properties of clouds and precipitation. Surprisingly, our analysis showed that the amount of ice turning into rain was lower than expected in the current product. To improve on this, we came up with a new way to extract information about the size and concentration of particles from radar data. As long as we use this method in the right conditions, we can even estimate how dense the ice is.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
EGUsphere, https://doi.org/10.5194/egusphere-2023-1722, https://doi.org/10.5194/egusphere-2023-1722, 2023
Short summary
Short summary
Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
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
Short summary
Short summary
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.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
Short summary
Short summary
We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
Qianqian Song, Zhibo Zhang, Hongbin Yu, Jasper F. Kok, Claudia Di Biagio, Samuel Albani, Jianyu Zheng, and Jiachen Ding
Atmos. Chem. Phys., 22, 13115–13135, https://doi.org/10.5194/acp-22-13115-2022, https://doi.org/10.5194/acp-22-13115-2022, 2022
Short summary
Short summary
This study developed a dataset that enables us to efficiently calculate dust direct radiative effect (DRE, i.e., cooling or warming our planet) for any given dust size distribution in addition to three sets of dust mineral components and two dust shapes. We demonstrate and validate the method of using this dataset to calculate dust DRE. Moreover, using this dataset we found that dust mineral composition is a more important factor in determining dust DRE than dust size and shape.
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
Short summary
Short summary
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.
Michael S. Diamond, Pablo E. Saide, Paquita Zuidema, Andrew S. Ackerman, Sarah J. Doherty, Ann M. Fridlind, Hamish Gordon, Calvin Howes, Jan Kazil, Takanobu Yamaguchi, Jianhao Zhang, Graham Feingold, and Robert Wood
Atmos. Chem. Phys., 22, 12113–12151, https://doi.org/10.5194/acp-22-12113-2022, https://doi.org/10.5194/acp-22-12113-2022, 2022
Short summary
Short summary
Smoke from southern Africa blankets the southeast Atlantic from June-October, overlying a major transition region between overcast and scattered clouds. The smoke affects Earth's radiation budget by absorbing sunlight and changing cloud properties. We investigate these effects in regional climate and large eddy simulation models based on international field campaigns. We find that large-scale circulation changes more strongly affect cloud transitions than smoke microphysical effects in our case.
Daniel Hernandez-Deckers, Toshihisa Matsui, and Ann M. Fridlind
Atmos. Chem. Phys., 22, 711–724, https://doi.org/10.5194/acp-22-711-2022, https://doi.org/10.5194/acp-22-711-2022, 2022
Short summary
Short summary
We investigate how the concentration of aerosols (small particles that serve as seeds for cloud droplets) affect the dynamics of simulated clouds using two different frameworks, i.e., the traditional selection of cloudy rising grid points and tracking small-scale coherent rising features (cumulus thermals). By doing so, we find that these cumulus thermals reveal useful information about the coupling between internal cloud circulations and cloud droplet and raindrop formation.
Florian Tornow, Andrew S. Ackerman, and Ann M. Fridlind
Atmos. Chem. Phys., 21, 12049–12067, https://doi.org/10.5194/acp-21-12049-2021, https://doi.org/10.5194/acp-21-12049-2021, 2021
Short summary
Short summary
Cold air outbreaks affect the local energy budget by forming bright boundary layer clouds that, once it rains, evolve into dimmer, broken cloud fields that are depleted of condensation nuclei – an evolution consistent with closed-to-open cell transitions. We find that cloud ice accelerates this evolution, primarily via riming prior to rain onset, which (1) reduces liquid water, (2) reduces condensation nuclei, and (3) leads to early precipitation cooling and moistening below cloud.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Richard Querel, Israel Silber, and Connor J. Flynn
Geosci. Model Dev., 14, 43–72, https://doi.org/10.5194/gmd-14-43-2021, https://doi.org/10.5194/gmd-14-43-2021, 2021
Robert Jackson, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell
Atmos. Meas. Tech., 14, 53–69, https://doi.org/10.5194/amt-14-53-2021, https://doi.org/10.5194/amt-14-53-2021, 2021
Short summary
Short summary
About 4 years of 2D video disdrometer data in Darwin are used to develop and validate rainfall retrievals for tropical convection in C- and X-band radars in Darwin. Using blended techniques previously used for Colorado and Manus and Gan islands, with modified coefficients in each estimator, provided the most optimal results. Using multiple radar observables to develop a rainfall retrieval provided a greater advantage than using a single observable, including using specific attenuation.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
Short summary
Short summary
Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Alexei Korolev, Ivan Heckman, Mengistu Wolde, Andrew S. Ackerman, Ann M. Fridlind, Luis A. Ladino, R. Paul Lawson, Jason Milbrandt, and Earle Williams
Atmos. Chem. Phys., 20, 1391–1429, https://doi.org/10.5194/acp-20-1391-2020, https://doi.org/10.5194/acp-20-1391-2020, 2020
Short summary
Short summary
This study attempts identification of mechanisms of secondary ice production (SIP) based on the observation of small faceted ice crystals. It was found that in both mesoscale convective systems and frontal clouds, SIP was observed right above the melting layer and extended to the higher altitudes with colder temperatures. A principal conclusion of this work is that the freezing drop shattering mechanism is plausibly accounting for the measured ice concentrations in the observed condition.
Keith M. Hines, David H. Bromwich, Sheng-Hung Wang, Israel Silber, Johannes Verlinde, and Dan Lubin
Atmos. Chem. Phys., 19, 12431–12454, https://doi.org/10.5194/acp-19-12431-2019, https://doi.org/10.5194/acp-19-12431-2019, 2019
Short summary
Short summary
We explore how well clouds are represented in numerical weather prediction over Antarctica, a very difficult environment for field programs where few studies have been conducted. Fortunately, a 2015–2017 field program for West Antarctica supplied observations. We achieve promising results with newer, more advanced cloud schemes. We need to understand the role of clouds and precipitation in the maintenance of the Antarctic ice mass to understand and predict sea level change over the 21st century.
Ann M. Fridlind, Marcus van Lier-Walqui, Scott Collis, Scott E. Giangrande, Robert C. Jackson, Xiaowen Li, Toshihisa Matsui, Richard Orville, Mark H. Picel, Daniel Rosenfeld, Alexander Ryzhkov, Richard Weitz, and Pengfei Zhang
Atmos. Meas. Tech., 12, 2979–3000, https://doi.org/10.5194/amt-12-2979-2019, https://doi.org/10.5194/amt-12-2979-2019, 2019
Short summary
Short summary
Weather radars are offering improved capabilities to investigate storm physics, which remain poorly understood. We investigate enhanced use of such data near Houston, Texas, where pollution sources often provide a convenient contrast between polluted and clean air. We conclude that Houston is a favorable location to conduct a future field campaign during June through September because isolated storms are common and tend to last an hour, allowing frequent observations of a full life cycle.
Grégory Cesana, Anthony D. Del Genio, Andrew S. Ackerman, Maxwell Kelley, Gregory Elsaesser, Ann M. Fridlind, Ye Cheng, and Mao-Sung Yao
Atmos. Chem. Phys., 19, 2813–2832, https://doi.org/10.5194/acp-19-2813-2019, https://doi.org/10.5194/acp-19-2813-2019, 2019
Short summary
Short summary
The response of low clouds to climate change (i.e., cloud feedbacks) is still pointed out as being the largest source of uncertainty in climate models. Here we use CALIPSO observations to discriminate climate models that reproduce observed interannual change of cloud fraction with SST forcings, referred to as a present-day cloud feedback. Modeling moist processes in the planetary boundary layer is crucial to produce large stratocumulus decks and realistic present-day cloud feedbacks.
Robert C. Jackson, Scott M. Collis, Valentin Louf, Alain Protat, and Leon Majewski
Atmos. Chem. Phys., 18, 17687–17704, https://doi.org/10.5194/acp-18-17687-2018, https://doi.org/10.5194/acp-18-17687-2018, 2018
Short summary
Short summary
This paper looks at a 17 year database of echo top heights of thunderstorms in Darwin retrieved by CPOL. We find that the echo top heights are generally bimodal, corresponding to cumulus congestus and deep convection, and show a greater bimodality during an inactive MJO. Furthermore, we find that convective cell areas are larger in break conditions compared to monsoon conditions, but only during MJO-inactive conditions.
Robert Jackson, Jeffrey R. French, David C. Leon, David M. Plummer, Sonia Lasher-Trapp, Alan M. Blyth, and Alexei Korolev
Atmos. Chem. Phys., 18, 15329–15344, https://doi.org/10.5194/acp-18-15329-2018, https://doi.org/10.5194/acp-18-15329-2018, 2018
Short summary
Short summary
This paper looks at microphysical observations of growing cumulus clouds in the southwest United Kingdom sampled during the COnvective Precipitation Experiment (COPE). Our results suggest that secondary ice production processes are contributing to the observed concentrations and that entrainment of particles from remnant cloud layers may have acted to aid in secondary ice production.
Katia Lamer, Ann M. Fridlind, Andrew S. Ackerman, Pavlos Kollias, Eugene E. Clothiaux, and Maxwell Kelley
Geosci. Model Dev., 11, 4195–4214, https://doi.org/10.5194/gmd-11-4195-2018, https://doi.org/10.5194/gmd-11-4195-2018, 2018
Short summary
Short summary
Weather and climate predictions of cloud, rain, and snow occurrence remain uncertain, in part because guidance from observation is incomplete. We present a tool that transforms predictions into observations from ground-based remote sensors. Liquid water and ice occurrence errors associated with the transformation are below 8 %, with ~ 3 % uncertainty. This (GO)2-SIM forward-simulator tool enables better evaluation of cloud, rain, and snow occurrence predictions using available observations.
Daniel J. Miller, Zhibo Zhang, Steven Platnick, Andrew S. Ackerman, Frank Werner, Celine Cornet, and Kirk Knobelspiesse
Atmos. Meas. Tech., 11, 3689–3715, https://doi.org/10.5194/amt-11-3689-2018, https://doi.org/10.5194/amt-11-3689-2018, 2018
Short summary
Short summary
Prior satellite comparisons of bispectral and polarimetric cloud droplet size retrievals exhibited systematic biases. However, similar airborne instrument retrievals have been found to be quite similar to one another. This study explains this discrepancy in terms of differing sensitivity to vertical profile, as well as spatial and angular resolution. This is accomplished by using a satellite retrieval simulator – an LES cloud model coupled to radiative transfer and cloud retrieval algorithms.
Spencer Faber, Jeffrey R. French, and Robert Jackson
Atmos. Meas. Tech., 11, 3645–3659, https://doi.org/10.5194/amt-11-3645-2018, https://doi.org/10.5194/amt-11-3645-2018, 2018
Short summary
Short summary
Laboratory and in-flight evaluations of uncertainties of measurements from a cloud droplet probe are presented. This study extends results of earlier studies by examining instrument response over a greater range of droplet sizes throughout the entire sample volume. Errors in droplet sizing based on the laboratory measurements tend to be less than 10 %, significantly less than typically quoted sizing accuracy for this class of instrument.
Xiaoli Zhou, Andrew S. Ackerman, Ann M. Fridlind, Robert Wood, and Pavlos Kollias
Atmos. Chem. Phys., 17, 12725–12742, https://doi.org/10.5194/acp-17-12725-2017, https://doi.org/10.5194/acp-17-12725-2017, 2017
Short summary
Short summary
Shallow maritime clouds make a well-known transition from stratocumulus to trade cumulus with flow from the subtropics equatorward. Three-day large-eddy simulations that investigate the potential influence of overlying African biomass burning plumes during that transition indicate that cloud-related impacts are likely substantially cooling to negligible at the top of the atmosphere, with magnitude sensitive to background and perturbation aerosol and cloud properties.
Kirk W. North, Mariko Oue, Pavlos Kollias, Scott E. Giangrande, Scott M. Collis, and Corey K. Potvin
Atmos. Meas. Tech., 10, 2785–2806, https://doi.org/10.5194/amt-10-2785-2017, https://doi.org/10.5194/amt-10-2785-2017, 2017
Short summary
Short summary
Vertical air motion retrievals from 3DVAR multiple distributed scanning Doppler radars are compared against collocated profiling radars and retrieved from an upward iteration integration iterative technique to characterize their veracity. The retrieved vertical air motions are generally within 1–2 m s−1 of agreement with profiling radars and better solution than the upward integration technique, and therefore can be used as a means to improve parameterizations in numerical models moving forward.
Ann M. Fridlind, Xiaowen Li, Di Wu, Marcus van Lier-Walqui, Andrew S. Ackerman, Wei-Kuo Tao, Greg M. McFarquhar, Wei Wu, Xiquan Dong, Jingyu Wang, Alexander Ryzhkov, Pengfei Zhang, Michael R. Poellot, Andrea Neumann, and Jason M. Tomlinson
Atmos. Chem. Phys., 17, 5947–5972, https://doi.org/10.5194/acp-17-5947-2017, https://doi.org/10.5194/acp-17-5947-2017, 2017
Short summary
Short summary
Understanding observed storm microphysics via computer simulation requires measurements of aerosol on which most hydrometeors form. We prepare aerosol input data for six storms observed over Oklahoma. We demonstrate their use in simulations of a case with widespread ice outflow well sampled by aircraft. Simulations predict too few ice crystals that are too large. We speculate that microphysics found in tropical storms occurred here, likely associated with poorly understood ice multiplication.
Yinghui Lu, Zhiyuan Jiang, Kultegin Aydin, Johannes Verlinde, Eugene E. Clothiaux, and Giovanni Botta
Atmos. Meas. Tech., 9, 5119–5134, https://doi.org/10.5194/amt-9-5119-2016, https://doi.org/10.5194/amt-9-5119-2016, 2016
Short summary
Short summary
The database contains the complete (polarimetric) scattering information for different types of ice particles at different incident and scattered radiation directions at four microwave wavelengths. These results are useful for understanding the dependence of ice-particle scattering properties on ice-particle orientation with respect to the incident and scattered radiation. It is also useful in ice-property retrievals, radar forward simulation.
Ann M. Fridlind, Rachel Atlas, Bastiaan van Diedenhoven, Junshik Um, Greg M. McFarquhar, Andrew S. Ackerman, Elisabeth J. Moyer, and R. Paul Lawson
Atmos. Chem. Phys., 16, 7251–7283, https://doi.org/10.5194/acp-16-7251-2016, https://doi.org/10.5194/acp-16-7251-2016, 2016
Short summary
Short summary
Images of crystals within mid-latitude cirrus clouds are used to derive consistent ice physical and optical properties for a detailed cloud microphysics model, including size-dependent mass, projected area, and fall speed. Based on habits found, properties are derived for bullet rosettes, their aggregates, and crystals with irregular shapes. Derived bullet rosette fall speeds are substantially greater than reported in past studies, owing to differences in mass, area, or diameter representation.
Israel Silber, Colin Price, and Craig J. Rodger
Atmos. Chem. Phys., 16, 3279–3288, https://doi.org/10.5194/acp-16-3279-2016, https://doi.org/10.5194/acp-16-3279-2016, 2016
Short summary
Short summary
We report for the first time that the semi-annual oscillation (SAO) is one of the dominant oscillations in the nighttime lower ionosphere, using ground-based measurements of VLF signals reflected off the lower part of the ionosphere. We conclude that the origins of this oscillation are oscillatory changes of the D region's electrical characteristics, driven by NOx transport from the lower thermosphere. This oscillation should be considered in lower ionospheric and VLF wave propagation models.
A. M. Fridlind, A. S. Ackerman, A. Grandin, F. Dezitter, M. Weber, J. W. Strapp, A. V. Korolev, and C. R. Williams
Atmos. Chem. Phys., 15, 11713–11728, https://doi.org/10.5194/acp-15-11713-2015, https://doi.org/10.5194/acp-15-11713-2015, 2015
Short summary
Short summary
Airbus measurements at elevations circa 11 km within large storm systems near Darwin and Santiago indicate ice mass distributed over area-equivalent diameters of 100-500 µm. Profiler-observed radar reflectivity and mean Doppler velocity under similar conditions are found to be consistent with measurements and with 1D simulations of steady-state stratiform rain columns initialized with observed ice size distributions. Results motivate investigation of ice formation pathways in Part II.
A. S. Ackerman, A. M. Fridlind, A. Grandin, F. Dezitter, M. Weber, J. W. Strapp, and A. V. Korolev
Atmos. Chem. Phys., 15, 11729–11751, https://doi.org/10.5194/acp-15-11729-2015, https://doi.org/10.5194/acp-15-11729-2015, 2015
Short summary
Short summary
An updraft parcel model with size-resolved microphysics is used to investigate microphysical pathways leading to ice water content > 2 g m-3 with mass median area-equivalent diameter of 200-300 micron reported at ~11 km in tropical deep convection. Parcel simulations require substantial source of small crystals at temperatures > ~-10 deg C growing by vapor deposition. Warm rain in weaker updrafts surprisingly leads to greater ice mass owing to reduced competition for available water vapor.
Related subject area
Climate and Earth system modeling
A sub-grid parameterization scheme for topographic vertical motion in CAM5-SE
Technology to aid the analysis of large-volume multi-institute climate model output at a central analysis facility (PRIMAVERA Data Management Tool V2.10)
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research
Monte Carlo drift correction – quantifying the drift uncertainty of global climate models
Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1
Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations
CIOFC1.0: a common parallel input/output framework based on C-Coupler2.0
Overcoming computational challenges to realize meter- to submeter-scale resolution in cloud simulations using the super-droplet method
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
URock 2023a: an open-source GIS-based wind model for complex urban settings
DASH: a MATLAB toolbox for paleoclimate data assimilation
Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPAS-Ocean (Version 7.1)
All aboard! Earth system investigations with the CH2O-CHOO TRAIN v1.0
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
The Teddy tool v1.1: temporal disaggregation of daily climate model data for climate impact analysis
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Truly Conserving with Conservative Remapping Methods
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
Earth system modeling on Modular Supercomputing Architectures: coupled atmosphere-ocean simulations with ICON 2.6.6-rc
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Understanding Changes in Cloud Simulations from E3SM Version 1 to Version 2
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
WRF (v4.0)-SUEWS (v2018c) Coupled System: Development, Evaluation and Application
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
Resolving the mesoscale at reduced computational cost with FESOM 2.5: efficient modeling approaches applied to the Southern Ocean
Modeling and evaluating the effects of irrigation on land-atmosphere interaction in South-West Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Deep Learning Model based on Multi-scale Feature Fusion for Precipitation Nowcasting
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
Yaqi Wang, Lanning Wang, Juan Feng, Zhenya Song, Qizhong Wu, and Huaqiong Cheng
Geosci. Model Dev., 16, 6857–6873, https://doi.org/10.5194/gmd-16-6857-2023, https://doi.org/10.5194/gmd-16-6857-2023, 2023
Short summary
Short summary
In this study, to noticeably improve precipitation simulation in steep mountains, we propose a sub-grid parameterization scheme for the topographic vertical motion in CAM5-SE to revise the original vertical velocity by adding the topographic vertical motion. The dynamic lifting effect of topography is extended from the lowest layer to multiple layers, thus improving the positive deviations of precipitation simulation in high-altitude regions and negative deviations in low-altitude regions.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev., 16, 6689–6700, https://doi.org/10.5194/gmd-16-6689-2023, https://doi.org/10.5194/gmd-16-6689-2023, 2023
Short summary
Short summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a central analysis facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large dataset. We believe that similar, multi-institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Maria-Theresia Pelz, Markus Schartau, Christopher J. Somes, Vanessa Lampe, and Thomas Slawig
Geosci. Model Dev., 16, 6609–6634, https://doi.org/10.5194/gmd-16-6609-2023, https://doi.org/10.5194/gmd-16-6609-2023, 2023
Short summary
Short summary
Kernel density estimators (KDE) approximate the probability density of a data set without the assumption of an underlying distribution. We used the solution of the diffusion equation, and a new approximation of the optimal smoothing parameter build on two pilot estimation steps, to construct such a KDE best suited for typical characteristics of geoscientific data. The resulting KDE is insensitive to noise and well resolves multimodal data structures as well as boundary-close data.
Benjamin S. Grandey, Zhi Yang Koh, Dhrubajyoti Samanta, Benjamin P. Horton, Justin Dauwels, and Lock Yue Chew
Geosci. Model Dev., 16, 6593–6608, https://doi.org/10.5194/gmd-16-6593-2023, https://doi.org/10.5194/gmd-16-6593-2023, 2023
Short summary
Short summary
Global climate models are susceptible to spurious trends known as drift. Fortunately, drift can be corrected when analysing data produced by models. To explore the uncertainty associated with drift correction, we develop a new method: Monte Carlo drift correction. For historical simulations of thermosteric sea level rise, drift uncertainty is relatively large. When analysing data susceptible to drift, researchers should consider drift uncertainty.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev., 16, 6355–6376, https://doi.org/10.5194/gmd-16-6355-2023, https://doi.org/10.5194/gmd-16-6355-2023, 2023
Short summary
Short summary
To assess the ability of Earth system model (ESM) predictions, we developed a tool called ESMAC Diags to understand how aerosols, clouds, and aerosol–cloud interactions are represented in ESMs. This paper describes its version 2 functionality. We compared the model predictions with measurements taken by planes, ships, satellites, and ground instruments over four regions across the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Xinzhu Yu, Li Liu, Chao Sun, Qingu Jiang, Biao Zhao, Zhiyuan Zhang, Hao Yu, and Bin Wang
Geosci. Model Dev., 16, 6285–6308, https://doi.org/10.5194/gmd-16-6285-2023, https://doi.org/10.5194/gmd-16-6285-2023, 2023
Short summary
Short summary
In this paper we propose a new common, flexible, and efficient parallel I/O framework for earth system modeling based on C-Coupler2.0. CIOFC1.0 can handle data I/O in parallel and provides a configuration file format that enables users to conveniently change the I/O configurations. It can automatically make grid and time interpolation, output data with an aperiodic time series, and accelerate data I/O when the field size is large.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
Short summary
Short summary
A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
Geosci. Model Dev., 16, 5755–5782, https://doi.org/10.5194/gmd-16-5755-2023, https://doi.org/10.5194/gmd-16-5755-2023, 2023
Short summary
Short summary
The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
Short summary
Short summary
The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
Short summary
Short summary
Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
Short summary
Short summary
Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
Short summary
Short summary
The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
Short summary
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
Short summary
Short summary
Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
Short summary
Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
Short summary
Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Karl E. Taylor
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-177, https://doi.org/10.5194/gmd-2023-177, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
Short summary
Short summary
A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
Short summary
Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
Short summary
Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
Short summary
Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
Short summary
Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
EGUsphere, https://doi.org/10.5194/egusphere-2023-1476, https://doi.org/10.5194/egusphere-2023-1476, 2023
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere-ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 59 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
Short summary
Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
Short summary
Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
Short summary
Short summary
This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
Short summary
Short summary
Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
Short summary
Short summary
A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
Short summary
Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1263, https://doi.org/10.5194/egusphere-2023-1263, 2023
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the E3SMv2 to document model performance on clouds and understand what updates in E3SMv2 have caused the changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved primarily due to the re-tuning of cloud macrophysics parameters. This study offers additional insights about clouds simulated in E3SMv2 and will benefit the future E3SM developments.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
Short summary
Short summary
In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
Short summary
Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-117, https://doi.org/10.5194/gmd-2023-117, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
Short summary
Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1496, https://doi.org/10.5194/egusphere-2023-1496, 2023
Short summary
Short summary
Ocean models struggle to simulate small-scale ocean flows due to the computational cost of high-resolution simulations. Several cost-reducing strategies are applied to simulations of the Southern Ocean and evaluated with respect to observations and traditional, lower-resolution modelling methods. The high-resolution simulations effectively reproduce small-scale flows seen in satellite data and are largely consistent with traditional model simulations regarding their response to climate change.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
EGUsphere, https://doi.org/10.5194/egusphere-2023-890, https://doi.org/10.5194/egusphere-2023-890, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The caused effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which is simulating the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
Short summary
Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
Short summary
Short summary
A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
Short summary
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
Short summary
Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-109, https://doi.org/10.5194/gmd-2023-109, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
1. This study present a deep learning architecture MFF to improve the forecast skills of precipitations especially for heavy precipitations. 2. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. 3. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors, so that heavy precipitations are produced.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
Short summary
Short summary
This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio Bento, and Angelina Bushenkova
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-136, https://doi.org/10.5194/gmd-2023-136, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data, and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
Short summary
Short summary
In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Cited articles
Amante, C.: ETOPO1 1 arc-minute global relief model: procedures, data sources
and analysis, NOAA Technical Memorandum NESDIS NGDC-24, National Geophysical
Data Center [data set], NOAA, Tech. rep., https://doi.org/10.7289/V5C8276M, 2009. a
Bodas-Salcedo, A., Webb, M. J., Brooks, M. E., Ringer, M. A., Williams, K. D.,
Milton, S. F., and Wilson, D. R.: Evaluating cloud systems in the Met Office
global forecast model using simulated CloudSat radar reflectivities, J.
Geophys. Res.-Atmos., 92, 1023–1043,
https://doi.org/10.1029/2007JD009620, 2008. a
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein,
S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.:
COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043,
https://doi.org/10.1175/2011BAMS2856.1, 2011. a, b
Bodas-Salcedo, A., Williams, K. D., Ringer, M. A., Beau, I., Cole, J. N. S.,
Dufresne, J.-L., Koshiro, T., Stevens, B., Wang, Z., and Yokohata, T.:
Origins of the Solar Radiation Biases over the Southern Ocean in CFMIP2
Models, J. Climate, 27, 41–56, https://doi.org/10.1175/JCLI-D-13-00169.1,
2014. a
Bohren, C. F. and Battan, L. J.: Radar Backscattering by Inhomogeneous
Precipitation Particles, J. Atmos. Sci., 37,
1821–1827, https://doi.org/10.1175/1520-0469(1980)037<1821:RBBIPP>2.0.CO;2, 1980. a
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small
Particles, John Wiley & Sons, Ltd, Weinheim, Germany,
https://doi.org/10.1002/9783527618156, 1983. a
Burns, D., Kollias, P., Tatarevic, A., Battaglia, A., and Tanelli, S.: The
performance of the EarthCARE Cloud Profiling Radar in marine stratiform
clouds, J. Geophys. Res.-Atmos., 121, 14525–14537,
https://doi.org/10.1002/2016JD025090, 2016. a, b
Campbell, J. R., Hlavka, D. L., Welton, E. J., Flynn, C. J., Turner, D. D.,
Spinhirne, J. D., Scott, V. S., and Hwang, I. H.: 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.CO2, 2002. a
Cesana, G. and Chepfer, H.: Evaluation of the cloud thermodynamic phase in a
climate model using CALIPSO-GOCCP, J. Geophys. Res.-Atmos., 118, 7922–7937, https://doi.org/10.1002/jgrd.50376, 2013. a, b
Cesana, G. and Waliser, D. E.: Characterizing and understanding systematic
biases in the vertical structure of clouds in CMIP5/CFMIP2 models,
Geophys. Res. Lett., 43, 10538–10546, https://doi.org/10.1002/2016GL070515,
2016. a
Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and de Boer, G.:
Ubiquitous low-level liquid-containing Arctic clouds: New observations and
climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett.,
39, L20804, https://doi.org/10.1029/2012GL053385, 2012. a
Cesana, G., Chepfer, H., Winker, D., Getzewich, B., Cai, X., Jourdan, O.,
Mioche, G., Okamoto, H., Hagihara, Y., Noel, V., and Reverdy, M.: Using in
situ airborne measurements to evaluate three cloud phase products derived
from CALIPSO, J. Geophys. Res.-Atmos., 121, 5788–5808,
https://doi.org/10.1002/2015JD024334, 2016. a
Cesana, G., Del Genio, A. D., Ackerman, A. S., Kelley, M., Elsaesser, G., Fridlind, A. M., Cheng, Y., and Yao, M.-S.: Evaluating models' response of tropical low clouds to SST forcings using CALIPSO observations, Atmos. Chem. Phys., 19, 2813–2832, https://doi.org/10.5194/acp-19-2813-2019, 2019. a, b, c
Cesana, G. V., Ackerman, A. S., Fridlind, A. M., Silber, I., and Kelley, M.:
Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud
Feedback, Geophys. Res. Lett., 48, e2021GL094876,
https://doi.org/10.1029/2021GL094876, 2021. a
Chen, Y.-S., Verlinde, J., Clothiaux, E. E., Ackerman, A. S., Fridlind, A. M.,
Chamecki, M., Kollias, P., Kirkpatrick, M. P., Chen, B.-C., Yu, G., and
Avramov, A.: On the forward modeling of radar Doppler spectrum width from
LES: Implications for model evaluation, J. Geophys. Res.-Atmos., 123, 7444–7461,
https://doi.org/10.1029/2017JD028104, 2018. a
Chepfer, H., Bony, S., Winker, D., Chiriaco, M., Dufresne, J.-L., and Sèze,
G.: Use of CALIPSO lidar observations to evaluate the cloudiness simulated by
a climate model, Geophys. Res. Lett., 35, L15704,
https://doi.org/10.1029/2008GL034207, 2008. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van
Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer,
C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E.,
Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch,
P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2),
J. Adv. Model. Earth Sy., 12, e2019MS001916,
https://doi.org/10.1029/2019MS001916, 2020. a
de Boer, G., Morrison, H., Shupe, M. D., and Hildner, R.: Evidence of liquid
dependent ice nucleation in high-latitude stratiform clouds from surface
remote sensors, Geophys. Res. Lett., 38, L01803,
https://doi.org/10.1029/2010GL046016, 2011. a
Derr, V. E., Abshire, N. L., Cupp, R. E., and McNice, G. T.: Depolarization of
Lidar Returns from Virga and Source Cloud, J. Appl. Meteorol.
Clim., 15, 1200–1203,
https://doi.org/10.1175/1520-0450(1976)015<1200:DOLRFV>2.0.CO;2, 1976. a, b
Ding, J., Bi, L., Yang, P., Kattawar, G. W., Weng, F., Liu, Q., and Greenwald,
T.: Single-scattering properties of ice particles in the microwave regime:
Temperature effect on the ice refractive index with implications in remote
sensing, J. Quant. Spectrosc. Ra., 190,
26–37, https://doi.org/10.1016/j.jqsrt.2016.11.026, 2017. a, b
Doviak, R. J. and Zrnić, D. S.: Doppler Radar and Weather Observations, 2nd Edn.
Academic Press, San Diego, https://doi.org/10.1016/C2009-0-22358-0,
1993. a
Eloranta, E. W.: Practical model for the calculation of multiply scattered
lidar returns, Appl. Optics, 37, 2464–2472, https://doi.org/10.1364/AO.37.002464, 1998. a
Eloranta, E. W.: High spectral resolution lidar, in: Lidar: Range-Resolved
Optical Remote Sensing of the Atmosphere, Springer New York,
New York, NY, 143–163, 2005. a
Elsaesser, G. S., Genio, A. D. D., Jiang, J. H., and van Lier-Walqui, M.: An
Improved Convective Ice Parameterization for the NASA GISS Global Climate
Model and Impacts on Cloud Ice Simulation, J. Climate, 30, 317–336, https://doi.org/10.1175/JCLI-D-16-0346.1, 2017. a
Falconi, M. T., von Lerber, A., Ori, D., Marzano, F. S., and Moisseev, D.: Snowfall retrieval at X, Ka and W bands: consistency of backscattering and microphysical properties using BAECC ground-based measurements, Atmos. Meas. Tech., 11, 3059–3079, https://doi.org/10.5194/amt-11-3059-2018, 2018. a, b
Fan, J., Ghan, S., Ovchinnikov, M., Liu, X., Rasch, P. J., and Korolev, A.:
Representation of Arctic mixed-phase clouds and the
Wegener-Bergeron-Findeisen process in climate models: Perspectives from a
cloud-resolving study, J. Geophys. Res.-Atmos., 116, D00T07,
https://doi.org/10.1029/2010JD015375, 2011. a
Flynn, C. J., Mendozaa, A., Zhengb, Y., and Mathurb, S.: Novel
polarization-sensitive micropulse lidar measurement technique, Opt.
Express, 15, 2785–2790, https://doi.org/10.1364/OE.15.002785, 2007. a
Fridlind, A. M. and Ackerman, A. S.: Simulations of Arctic
Mixed-Phase Boundary Layer Clouds: Advances in Understanding and Outstanding
Questions, chap. 7, in: Mixed-Phase Clouds, edited by: Andronache, C.,
Elsevier, 153–183, https://doi.org/10.1016/B978-0-12-810549-8.00007-6, 2018. a
Gettelman, A. and Morrison, H.: Advanced Two-Moment Bulk Microphysics for
Global Models, Part I: Off-Line Tests and Comparison with Other Schemes,
J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015. a, b, c
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke,
M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar,
J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman,
M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones,
P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y.,
Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov,
A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y.,
Rasch, P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts,
A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B.,
Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan,
H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie,
S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang,
C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM
Coupled Model Version 1: Overview and Evaluation at Standard Resolution,
J. Adv. Model. Earth Sy., 11, 2089–2129,
https://doi.org/10.1029/2018MS001603, 2019. a
Hansen, J. E.: Multiple Scattering of Polarized Light in Planetary Atmospheres
Part II. Sunlight Reflected by Terrestrial Water Clouds, J.
Atmos. Sci., 28, 1400–1426,
https://doi.org/10.1175/1520-0469(1971)028<1400:MSOPLI>2.0.CO;2, 1971. a
Heiblum, R. H., Altaratz, O., Koren, I., Feingold, G., Kostinski, A. B., Khain,
A. P., Ovchinnikov, M., Fredj, E., Dagan, G., Pinto, L., Yaish, R., and Chen,
Q.: Characterization of cumulus cloud fields using trajectories in the center
of gravity versus water mass phase space: 2. Aerosol effects on warm
convective clouds, J. Geophys. Res.-Atmos., 121,
6356–6373, https://doi.org/10.1002/2015JD024193, 2016. a
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
Hillman, B. R., Marchand, R. T., and Ackerman, T. P.: Sensitivities of
Simulated Satellite Views of Clouds to Subgrid-Scale Overlap and Condensate
Heterogeneity, J. Geophys. Res.-Atmos., 123, 7506–7529,
https://doi.org/10.1029/2017JD027680, 2018. a, b
Holz, R. E., Platnick, S., Meyer, K., Vaughan, M., Heidinger, A., Yang, P., Wind, G., Dutcher, S., Ackerman, S., Amarasinghe, N., Nagle, F., and Wang, C.: Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals, Atmos. Chem. Phys., 16, 5075–5090, https://doi.org/10.5194/acp-16-5075-2016, 2016. a
Hoyer, S. and Hamman, J.: xarray: N-D labeled arrays and datasets in
Python, J. Open Res. Softw., 5, p. 10, https://doi.org/10.5334/jors.148, 2017. a
Khairoutdinov, M. and Kogan, Y.: A New Cloud Physics Parameterization in a
Large-Eddy Simulation Model of Marine Stratocumulus, Mon. Weather Rev.,
128, 229–243, https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2, 2000. 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
Klein, S. A., Zhang, Y., Zelinka, M. D., Pincus, R., Boyle, J., and Gleckler,
P. J.: Are climate model simulations of clouds improving? An evaluation using
the ISCCP simulator, J. Geophys. Res.-Atmos., 118,
1329–1342, https://doi.org/10.1002/jgrd.50141, 2013. a, b
Kuma, P., McDonald, A. J., Morgenstern, O., Querel, R., Silber, I., and Flynn, C. J.: Ground-based lidar processing and simulator framework for comparing models and observations (ALCF 1.0), Geosci. Model Dev., 14, 43–72, https://doi.org/10.5194/gmd-14-43-2021, 2021. a
Lin, J.-L., Qian, T., and Shinoda, T.: Stratocumulus Clouds in Southeastern
Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models, J.
Climate, 27, 3000–3022, https://doi.org/10.1175/JCLI-D-13-00376.1, 2014. a
Lin, L., Gettelman, A., Xu, Y., Wu, C., Wang, Z., Rosenbloom, N., Bates, S. C., and Dong, W.: CAM6 simulation of mean and extreme precipitation over Asia: sensitivity to upgraded physical parameterizations and higher horizontal resolution, Geosci. Model Dev., 12, 3773–3793, https://doi.org/10.5194/gmd-12-3773-2019, 2019. a, b
Lubin, D., Zhang, D., Silber, I., Scott, R. C., Kalogeras, P., Battaglia, A.,
Bromwich, D. H., Cadeddu, M., Eloranta, E., Fridlind, A., Frossard, A.,
Hines, K. M., Kneifel, S., Leaitch, W. R., Lin, W., Nicolas, J., Powers, H.,
Quinn, P. K., Rowe, P., Russell, L. M., Sharma, S., Verlinde, J., and
Vogelmann, A. M.: AWARE: The Atmospheric Radiation Measurement (ARM) West
Antarctic Radiation Experiment, B. Am. Meteorol.
Soc., 101, E1069–E1091, https://doi.org/10.1175/BAMS-D-18-0278.1, 2020. a
Mätzler, C. (Ed.): Thermal Microwave Radiation: Applications for Remote
Sensing, Electromagnetic Waves, Institution of Engineering and Technology,
available at: https://digital-library.theiet.org/content/books/ew/pbew052e (last access: 1 December 2021),
2006. a
Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020. a
Morris, V. R.: Ceilometer Instrument Handbook, DOE/SC-ARM-TR-020, DOE Office
of Science, Office of Biological and Environmental Research, https://doi.org/10.2172/1036530, 2016. a
Morrison, H. and Gettelman, A.: A New Two-Moment Bulk Stratiform Cloud
Microphysics Scheme in the Community Atmosphere Model, Version 3 (CAM3). Part
I: Description and Numerical Tests, J. Climate, 21, 3642–3659,
https://doi.org/10.1175/2008JCLI2105.1, 2008. a, b, c, d
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on
the Development of Trailing Stratiform Precipitation in a Simulated Squall
Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev.,
137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and
Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat.
Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012. a
Myers, T. A., Scott, R. C., Zelinka, M. D., Klein, S. A., Norris, J. R., and
Caldwell, P. M.: Observational constraints on low cloud feedback reduce
uncertainty of climate sensitivity, Nat. Clim. Change, 11, 501–507,
https://doi.org/10.1038/s41558-021-01039-0, 2021. a
Newsom, R. K.: Raman Lidar (RL) Handbook, DOE/SC-ARM-TR-038, DOE Office of
Science, Office of Biological and Environmental Research, United States,
https://doi.org/10.2172/1020561, 2009. a
Noel, V., Roy, G., Bissonnette, L., Chepfer, H., and Flamant, P.: Analysis of
lidar measurements of ice clouds at multiple incidence angles, Geophys.
Res. Lett., 29, 52-1–52-4,
https://doi.org/10.1029/2002GL014828, 2002. a
Nott, G. J. and Duck, T. J.: Lidar studies of the polar troposphere,
Meteorol. Appl., 18, 383–405, https://doi.org/10.1002/met.289, 2011. a
Oue, M., Tatarevic, A., Kollias, P., Wang, D., Yu, K., and Vogelmann, A. M.: The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory, Geosci. Model Dev., 13, 1975–1998, https://doi.org/10.5194/gmd-13-1975-2020, 2020. a
Penndorf, R.: Tables of the Refractive Index for Standard Air and the Rayleigh
Scattering Coefficient for the Spectral Region between 0.2 and 20.0 µ and
Their Application to Atmospheric Optics, J. Opt. Soc.
Am., 47, 176–182, https://doi.org/10.1364/JOSA.47.000176, 1957. a
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum,
B. A., Riedi, J. C., and Frey, R. A.: The MODIS cloud products:
algorithms and examples from Terra, IEEE T. Geosci.
Remote, 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003. a
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N.,
Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz,
R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical
and Microphysical Products: Collection 6 Updates and Examples From Terra and
Aqua, IEEE T. Geosci. Remote, 55, 502–525,
https://doi.org/10.1109/TGRS.2016.2610522, 2017. a
Razenkov, I. I. and Eloranta, E. W.: High spectral resolution lidar at the
university of wisconsin-madison, EPJ Web Conf., 176, 01024,
https://doi.org/10.1051/epjconf/201817601024, 2018. a
Rowe, P. M., Fergoda, M., and Neshyba, S.: Temperature-Dependent Optical
Properties of Liquid Water From 240 to 298 K, J. Geophys. Res.-Atmos., 125, e2020JD032624,
https://doi.org/10.1029/2020JD032624,2020. a
Rémillard, J. and Tselioudis, G.: Cloud Regime Variability over the Azores
and Its Application to Climate Model Evaluation, J. Climate, 28,
9707–9720, https://doi.org/10.1175/JCLI-D-15-0066.1, 2015. a
Sassen, K.: Polarization in lidar: a review, in: Polarization Science and
Remote Sensing, edited by: Shaw, J. A. and Tyo, J. S., International Society for Optics and Photonics, SPIE, 5158, 151–160,
https://doi.org/10.1117/12.507006, 2003. a
Seifert, A. and Beheng, K. D.: A double-moment parameterization for simulating
autoconversion, accretion and selfcollection, Atmos. Res., 59–60,
265–281, https://doi.org/10.1016/S0169-8095(01)00126-0, 2001. a
Shupe, M. D.: A ground-based multisensor cloud phase classifier, Geophys.
Res. Lett., 34, L22809, https://doi.org/10.1029/2007GL031008, 2007. a
Silber, I.: AWARE Highly Supercooled Cloud Case Study Model Initialization Files for SCMs, Mendeley Data V1 [data set], https://doi.org/10.17632/gz4gdn3jvz.1, 2021. a, b
Silber, I., Verlinde, J., Eloranta, E. W., and Cadeddu, M.: Antarctic cloud
macrophysical, thermodynamic phase, and atmospheric inversion coupling
properties at McMurdo Station, Part I: Principal data processing and
climatology, J. Geophys. Res.-Atmos., 123, 6099–6121,
https://doi.org/10.1029/2018JD028279, 2018a. a, b
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, 2018b. a, b
Silber, I., Fridlind, A. M., Verlinde, J., Ackerman, A. S., Chen, Y.-S.,
Bromwich, D. H., Wang, S.-H., Cadeddu, M., and Eloranta, E. W.: Persistent
Supercooled Drizzle at Temperatures Below −25 ∘C Observed at McMurdo
Station, Antarctica, J. Geophys. Res.-Atmos., 124,
10878–10895, https://doi.org/10.1029/2019JD030882, 2019a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Silber, I., Verlinde, J., Wang, S.-H., Bromwich, D. H., Fridlind, A. M.,
Cadeddu, M., Eloranta, E. W., and Flynn, C. J.: Cloud Influence on ERA5 and
AMPS Surface Downwelling Longwave Radiation Biases in West Antarctica,
J. Climate, 32, 7935–7949, https://doi.org/10.1175/JCLI-D-19-0149.1,
2019b. a
Silber, I., Fridlind, A. M., Verlinde, J., Russell, L. M., and Ackerman, A. S.:
Nonturbulent Liquid-Bearing Polar Clouds: Observed Frequency of Occurrence
and Simulated Sensitivity to Gravity Waves, Geophys. Res. Lett., 47,
e2020GL087099, https://doi.org/10.1029/2020GL087099,
2020. a, b
Silber, I., Fridlind, A. M., Verlinde, J., Ackerman, A. S., Cesana, G. V., and Knopf, D. A.: The prevalence of precipitation from polar supercooled clouds, Atmos. Chem. Phys., 21, 3949–3971, https://doi.org/10.5194/acp-21-3949-2021, 2021a. a, b
Silber, I., Jackson, R. C., Fridlind, A. M., Ackerman, A. S., Collis, S.,
Verlinde, J., and Ding, J.:
Silber_et_al_EMC²_gmd-2021-194_code_and_data, Zenodo [code],
https://doi.org/10.5281/zenodo.5115252, 2021b. a
Silber, I., McGlynn, P. S., Harrington, J. Y., and Verlinde, J.:
Habit-Dependent Vapor Growth Modulates Arctic Supercooled Water Occurrence,
Geophys. Res. Lett., 48, e2021GL092767,
https://doi.org/10.1029/2021GL092767,
2021c. a
Smith, R. N. B.: A scheme for predicting layer clouds and their water content
in a general circulation model, Q. J. Roy. Meteor.
Soc., 116, 435–460, https://doi.org/10.1002/qj.49711649210, 1990. a
Sokolowsky, G. A., Clothiaux, E. E., Baggett, C. F., Lee, S., Feldstein, S. B.,
Eloranta, E. W., Cadeddu, M. P., Bharadwaj, N., and Johnson, K. L.:
Contributions to the Surface Downwelling Longwave Irradiance during Arctic
Winter at Utqiaġvik (Barrow) Alaska, J. Climate, 33, 4555–4577,
https://doi.org/10.1175/JCLI-D-18-0876.1, 2020. a
Stephens, G. L., L'Ecuyer, T., Forbes, R., Gettelmen, A., Golaz, J.-C.,
Bodas-Salcedo, A., Suzuki, K., Gabriel, P., and Haynes, J.: Dreary state of
precipitation in global models, J. Geophys. Res.-Atmos., 115, D24211,
115, https://doi.org/10.1029/2010JD014532, 2010. a
Stevens, D. E., Ackerman, A. S., and Bretherton, C. S.: Effects of Domain Size
and Numerical Resolution on the Simulation of Shallow Cumulus Convection,
J. Atmos. Sci., 59, 3285–3301,
https://doi.org/10.1175/1520-0469(2002)059<3285:EODSAN>2.0.CO;2, 2002. a
Suzuki, K., Stephens, G., Bodas-Salcedo, A., Wang, M., Golaz, J.-C., Yokohata,
T., and Koshiro, T.: Evaluation of the Warm Rain Formation Process in Global
Models with Satellite Observations, J. Atmos. Sci., 72,
3996–4014, https://doi.org/10.1175/JAS-D-14-0265.1, 2015. a
Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geosci. Model Dev., 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, 2018. a, b
Tan, I. and Storelvmo, T.: Sensitivity Study on the Influence of Cloud
Microphysical Parameters on Mixed-Phase Cloud Thermodynamic Phase
Partitioning in CAM5, J. Atmos. Sci., 73, 709–728,
https://doi.org/10.1175/JAS-D-15-0152.1, 2016. a
Theisen, A., Kehoe, K., Jackson, B., Sherman, Z., Godine, C., ajsockol,
jkyrouac, and Hemedinger, J.: ARM-DOE/ACT: Version 0.4.3, Zenodo [code],
https://doi.org/10.5281/zenodo.3987372, 2020. a
Thorsen, T. J. and Fu, Q.: Automated Retrieval of Cloud and Aerosol Properties
from the ARM Raman Lidar. Part II: Extinction, J. Atmos.
Ocean. Tech., 32, 1999–2023, https://doi.org/10.1175/JTECH-D-14-00178.1, 2015. a, b, c
Tian, L. and Curry, J. A.: Cloud overlap statistics, J. Geophys.
Res.-Atmos., 94, 9925–9935,
https://doi.org/10.1029/JD094iD07p09925, 1989. a
Turner, D. D., Kneifel, S., and Cadeddu, M. P.: An Improved Liquid Water
Absorption Model at Microwave Frequencies for Supercooled Liquid Water
Clouds, J. Atmos. Ocean. Tech., 33, 33–44,
https://doi.org/10.1175/JTECH-D-15-0074.1, 2016. a
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing:
Microwave remote sensing fundamentals and radiometry, Artech House, Norwood, Massachusetts
1st Edn., 1981. a
Vassel, M., Ickes, L., Maturilli, M., and Hoose, C.: Classification of Arctic multilayer clouds using radiosonde and radar data in Svalbard, Atmos. Chem. Phys., 19, 5111–5126, https://doi.org/10.5194/acp-19-5111-2019, 2019. a, b
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019. a
Warren, S. G. and Brandt, R. E.: Optical constants of ice from the ultraviolet
to the microwave: A revised compilation, J. Geophys. Res.-Atmos., 113, D14220, https://doi.org/10.1029/2007JD009744, 2008. 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
Widener, K. and Bharadwaj, N.: C-Band Scanning ARM Precipitation Radar
(C-SAPR) Handbook, ARM-TR-121, DOE Office of Science, Office of Biological
and Environmental Research, https://doi.org/10.2172/1054629, 2012. a
Widener, K. and Johnson, K.: W-band ARM Cloud Radar (WACR) Handbook,
ARM-TR-073, DOE Office of Science, Office of Biological and Environmental
Research, United States, https://doi.org/10.2172/1019541, 2006. a, b, c
Widener, K., Bharadwaj, N., and Johnson, K.: Ka-Band ARM Zenith Radar (KAZR)
Instrument Handbook, ARM-TR-106, DOE Office of Science, Office of Biological
and Environmental Research, United States, https://doi.org/10.2172/1035855,
2012a. a, b, c
Widener, K. B. and Mead, J. B.: W-band ARM cloud radar – Specifications and
design, in: Proc. 14th ARM Science Team Meeting, Albuquerque, New Mexico, 2004. a
Widener, K. B., Bharadwaj, N., and Johnson, K.: Scanning ARM Cloud Radar
(X/Ka/W-SACR), ARM-TR-113, DOE Office of Science, Office of Biological and
Environmental Research, United States, https://doi.org/10.2172/1043296,
2012b. a, b, c
Winker, D. M.: Accounting for multiple scattering in retrievals from space
lidar, in: 12th International Workshop on Lidar Multiple Scattering
Experiments, edited by: Werner, C., Oppel, U. G., and Rother, T., International Society for Optics and Photonics, SPIE, vol. 5059,
128–139,
https://doi.org/10.1117/12.512352, 2003. 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
Xiao, H., Zhao, P., Liu, X., and Li, H.: Sensitivity of aerosol-cloud
interactions to autoconversion schemes in mixed-phase orographic clouds,
Atmos. Res., 247, 105205,
https://doi.org/10.1016/j.atmosres.2020.105205, 2021. a
Yang, P., Bi, L., Baum, B. A., Liou, K.-N., Kattawar, G. W., Mishchenko, M. I.,
and Cole, B.: Spectrally Consistent Scattering, Absorption, and Polarization
Properties of Atmospheric Ice Crystals at Wavelengths from 0.2 to 100 µm,
J. Atmos. Sci., 70, 330–347,
https://doi.org/10.1175/JAS-D-12-039.1, 2013. a, b, c
Zaremba, T. J., Rauber, R. M., McFarquhar, G. M., Hayman, M., Finlon, J. A.,
and Stechman, D. M.: Phase Characterization of Cold Sector Southern Ocean
Cloud Tops: Results From SOCRATES, J. Geophys. Res.-Atmos., 125, e2020JD033673,
https://doi.org/10.1029/2020JD033673, 2020. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M.,
Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate
Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47,
e2019GL085782, https://doi.org/10.1029/2019GL085782,2020. 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
Short summary
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.
The Earth Model Column Collaboratory (EMC2) is an open-source ground-based (and air- or...