Articles | Volume 23, issue 1
https://doi.org/10.5194/acp-23-523-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-523-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Machine learning of cloud types in satellite observations and climate models
Department of Meteorology (MISU), Stockholm University, Stockholm, Sweden
Frida A.-M. Bender
Department of Meteorology (MISU), Stockholm University, Stockholm, Sweden
Alex Schuddeboom
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa New Zealand
Adrian J. McDonald
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa New Zealand
Øyvind Seland
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
Related authors
Zhangcheng Pei, Sonya L. Fiddes, W. John R. French, Simon P. Alexander, Marc D. Mallet, Peter Kuma, and Adrian McDonald
Atmos. Chem. Phys., 23, 14691–14714, https://doi.org/10.5194/acp-23-14691-2023, https://doi.org/10.5194/acp-23-14691-2023, 2023
Short summary
Short summary
In this paper, we use ground-based observations to evaluate a climate model and a satellite product in simulating surface radiation and investigate how radiation biases are influenced by cloud properties over the Southern Ocean. We find that significant radiation biases exist in both the model and satellite. The cloud fraction and cloud occurrence play an important role in affecting radiation biases. We suggest further development for the model and satellite using ground-based observations.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
Short summary
Short summary
Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
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.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
Short summary
Short summary
MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
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
Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Simon P. Alexander, John J. Cassano, Sally Garrett, Jamie Halla, Sean Hartery, Mike J. Harvey, Simon Parsons, Graeme Plank, Vidya Varma, and Jonny Williams
Atmos. Chem. Phys., 20, 6607–6630, https://doi.org/10.5194/acp-20-6607-2020, https://doi.org/10.5194/acp-20-6607-2020, 2020
Short summary
Short summary
We evaluate clouds over the Southern Ocean in the climate model HadGEM3 and reanalysis MERRA-2 using ship-based ceilometer and radiosonde observations. We find the models underestimate cloud cover by 18–25 %, with clouds below 2 km dominant in reality but lacking in the models. We find a strong link between clouds, atmospheric stability and sea surface temperature in observations but not in the models, implying that sub-grid processes do not generate enough cloud in response to these conditions.
Ben Jolly, Peter Kuma, Adrian McDonald, and Simon Parsons
Atmos. Chem. Phys., 18, 9723–9739, https://doi.org/10.5194/acp-18-9723-2018, https://doi.org/10.5194/acp-18-9723-2018, 2018
Short summary
Short summary
Clouds in the Ross Sea and Ross Ice Shelf regions are examined using a combination of satellite observations from the CloudSat and CALIPSO satellite datasets. We show that previous studies may have included an artefact at high altitudes which under-estimated cloud occurrence. We also find that the meteorological regime is a stronger control of cloud occurrence, cloud type and cloud top than season over this region, though season is a strong control on the phase of cloud.
Alejandro Baró Pérez, Michael S. Diamond, Frida A.-M. Bender, Abhay Devasthale, Matthias Schwarz, Julien Savre, Juha Tonttila, Harri Kokkola, Hyunho Lee, David Painemal, and Annica M. L. Ekman
Atmos. Chem. Phys., 24, 4591–4610, https://doi.org/10.5194/acp-24-4591-2024, https://doi.org/10.5194/acp-24-4591-2024, 2024
Short summary
Short summary
We use a numerical model to study interactions between humid light-absorbing aerosol plumes, clouds, and radiation over the southeast Atlantic. We find that the warming produced by the aerosols reduces cloud cover, especially in highly polluted situations. Aerosol impacts on drizzle play a minor role. However, aerosol effects on cloud reflectivity and moisture-induced changes in cloud cover dominate the climatic response and lead to an overall cooling by the biomass burning plumes.
Yusuf A. Bhatti, Laura E. Revell, Alex J. Schuddeboom, Adrian J. McDonald, Alex T. Archibald, Jonny Williams, Abhijith U. Venugopal, Catherine Hardacre, and Erik Behrens
Atmos. Chem. Phys., 23, 15181–15196, https://doi.org/10.5194/acp-23-15181-2023, https://doi.org/10.5194/acp-23-15181-2023, 2023
Short summary
Short summary
Aerosols are a large source of uncertainty over the Southern Ocean. A dominant source of sulfate aerosol in this region is dimethyl sulfide (DMS), which is poorly simulated by climate models. We show the sensitivity of simulated atmospheric DMS to the choice of oceanic DMS data set and emission scheme. We show that oceanic DMS has twice the influence on atmospheric DMS than the emission scheme. Simulating DMS more accurately in climate models will help to constrain aerosol uncertainty.
Zhangcheng Pei, Sonya L. Fiddes, W. John R. French, Simon P. Alexander, Marc D. Mallet, Peter Kuma, and Adrian McDonald
Atmos. Chem. Phys., 23, 14691–14714, https://doi.org/10.5194/acp-23-14691-2023, https://doi.org/10.5194/acp-23-14691-2023, 2023
Short summary
Short summary
In this paper, we use ground-based observations to evaluate a climate model and a satellite product in simulating surface radiation and investigate how radiation biases are influenced by cloud properties over the Southern Ocean. We find that significant radiation biases exist in both the model and satellite. The cloud fraction and cloud occurrence play an important role in affecting radiation biases. We suggest further development for the model and satellite using ground-based observations.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie Hammer, Larry Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
EGUsphere, https://doi.org/10.5194/egusphere-2023-2370, https://doi.org/10.5194/egusphere-2023-2370, 2023
Short summary
Short summary
We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its five components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce well the observed spatial pattern for OC, sulfate, nitrate and ammonium, yet the agreement is poorer for BC.
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.
Sushant Das, Frida Bender, and Thorsten Mauritsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1605, https://doi.org/10.5194/egusphere-2023-1605, 2023
Short summary
Short summary
Quantifying global and Indian precipitation responses to anthropogenic aerosol and CO2 forcings using multiple models is needed for reducing climate uncertainty. The response to global warming from CO2 increases precipitation both globally and over India, whereas the cooling response to sulfate aerosol leads to a reduction in precipitation in both cases. An opposite response to black carbon is noted i.e., a global decrease but an increase of precipitation over India implying changes in dynamics.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
EGUsphere, https://doi.org/10.5194/egusphere-2023-1085, https://doi.org/10.5194/egusphere-2023-1085, 2023
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Astrid Fremme, Paul J. Hezel, Øyvind Seland, and Harald Sodemann
Weather Clim. Dynam., 4, 449–470, https://doi.org/10.5194/wcd-4-449-2023, https://doi.org/10.5194/wcd-4-449-2023, 2023
Short summary
Short summary
We study the atmospheric moisture transport into eastern China for past, present, and future climate. Hence, we use different climate and weather prediction model data with a moisture source identification method. We find that while the moisture to first order originates mostly from similar regions, smaller changes consistently point to differences in the recycling of precipitation over land between different climates. Some differences are larger between models than between different climates.
Aiden R. Jönsson and Frida A.-M. Bender
Earth Syst. Dynam., 14, 345–365, https://doi.org/10.5194/esd-14-345-2023, https://doi.org/10.5194/esd-14-345-2023, 2023
Short summary
Short summary
The Earth has nearly the same mean albedo in both hemispheres, a feature not well replicated by climate models. Global warming causes changes in surface and cloud properties that affect albedo and that feed back into the warming. We show that models predict more darkening due to ice loss in the Northern than in the Southern Hemisphere in response to increasing CO2 concentrations. This is, to varying degrees, counteracted by changes in cloud cover, with implications for cloud feedback on climate.
Ville Leinonen, Harri Kokkola, Taina Yli-Juuti, Tero Mielonen, Thomas Kühn, Tuomo Nieminen, Simo Heikkinen, Tuuli Miinalainen, Tommi Bergman, Ken Carslaw, Stefano Decesari, Markus Fiebig, Tareq Hussein, Niku Kivekäs, Radovan Krejci, Markku Kulmala, Ari Leskinen, Andreas Massling, Nikos Mihalopoulos, Jane P. Mulcahy, Steffen M. Noe, Twan van Noije, Fiona M. O'Connor, Colin O'Dowd, Dirk Olivie, Jakob B. Pernov, Tuukka Petäjä, Øyvind Seland, Michael Schulz, Catherine E. Scott, Henrik Skov, Erik Swietlicki, Thomas Tuch, Alfred Wiedensohler, Annele Virtanen, and Santtu Mikkonen
Atmos. Chem. Phys., 22, 12873–12905, https://doi.org/10.5194/acp-22-12873-2022, https://doi.org/10.5194/acp-22-12873-2022, 2022
Short summary
Short summary
We provide the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five different earth system models. We investigated aerosol modes (nucleation, Aitken, and accumulation) separately and were able to show the differences between measured and modeled trends and especially their seasonal patterns. The differences in model results are likely due to complex effects of several processes instead of certain specific model features.
Petri Räisänen, Joonas Merikanto, Risto Makkonen, Mikko Savolahti, Alf Kirkevåg, Maria Sand, Øyvind Seland, and Antti-Ilari Partanen
Atmos. Chem. Phys., 22, 11579–11602, https://doi.org/10.5194/acp-22-11579-2022, https://doi.org/10.5194/acp-22-11579-2022, 2022
Short summary
Short summary
A climate model is used to evaluate how the radiative forcing (RF) associated with black carbon (BC) emissions depends on the latitude, longitude, and seasonality of emissions. It is found that both the direct RF (BC absorption of solar radiation in air) and snow RF (BC absorption in snow/ice) depend strongly on the emission region and season. The results suggest that, for a given mass of BC emitted, climatic impacts are likely to be largest for high-latitude emissions due to the large snow RF.
Qirui Zhong, Nick Schutgens, Guido van der Werf, Twan van Noije, Kostas Tsigaridis, Susanne E. Bauer, Tero Mielonen, Alf Kirkevåg, Øyvind Seland, Harri Kokkola, Ramiro Checa-Garcia, David Neubauer, Zak Kipling, Hitoshi Matsui, Paul Ginoux, Toshihiko Takemura, Philippe Le Sager, Samuel Rémy, Huisheng Bian, Mian Chin, Kai Zhang, Jialei Zhu, Svetlana G. Tsyro, Gabriele Curci, Anna Protonotariou, Ben Johnson, Joyce E. Penner, Nicolas Bellouin, Ragnhild B. Skeie, and Gunnar Myhre
Atmos. Chem. Phys., 22, 11009–11032, https://doi.org/10.5194/acp-22-11009-2022, https://doi.org/10.5194/acp-22-11009-2022, 2022
Short summary
Short summary
Aerosol optical depth (AOD) errors for biomass burning aerosol (BBA) are evaluated in 18 global models against satellite datasets. Notwithstanding biases in satellite products, they allow model evaluations. We observe large and diverse model biases due to errors in BBA. Further interpretations of AOD diversities suggest large biases exist in key processes for BBA which require better constraining. These results can contribute to further model improvement and development.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
Short summary
Short summary
Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Alex R. Aves, Laura E. Revell, Sally Gaw, Helena Ruffell, Alex Schuddeboom, Ngaire E. Wotherspoon, Michelle LaRue, and Adrian J. McDonald
The Cryosphere, 16, 2127–2145, https://doi.org/10.5194/tc-16-2127-2022, https://doi.org/10.5194/tc-16-2127-2022, 2022
Short summary
Short summary
This study confirms the presence of microplastics in Antarctic snow, highlighting the extent of plastic pollution globally. Fresh snow was collected from Ross Island, Antarctica, and subsequent analysis identified an average of 29 microplastic particles per litre of melted snow. The most likely source of these airborne microplastics is local scientific research stations; however, modelling shows their origin could have been up to 6000 km away.
Ingo Bethke, Yiguo Wang, François Counillon, Noel Keenlyside, Madlen Kimmritz, Filippa Fransner, Annette Samuelsen, Helene Langehaug, Lea Svendsen, Ping-Gin Chiu, Leilane Passos, Mats Bentsen, Chuncheng Guo, Alok Gupta, Jerry Tjiputra, Alf Kirkevåg, Dirk Olivié, Øyvind Seland, Julie Solsvik Vågane, Yuanchao Fan, and Tor Eldevik
Geosci. Model Dev., 14, 7073–7116, https://doi.org/10.5194/gmd-14-7073-2021, https://doi.org/10.5194/gmd-14-7073-2021, 2021
Short summary
Short summary
The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.
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.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
Short summary
Short summary
MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
Alejandro Baró Pérez, Abhay Devasthale, Frida A.-M. Bender, and Annica M. L. Ekman
Atmos. Chem. Phys., 21, 6053–6077, https://doi.org/10.5194/acp-21-6053-2021, https://doi.org/10.5194/acp-21-6053-2021, 2021
Short summary
Short summary
We study the impacts of above-cloud biomass burning plumes on radiation and clouds over the southeast Atlantic using data derived from satellite observations and data-constrained model simulations. A substantial amount of the aerosol within the plumes is not classified as smoke by the satellite. The atmosphere warms more with increasing smoke aerosol loading. No clear influence of aerosol type, loading, or moisture within the overlying aerosol plumes is detected on the cloud top cooling rates.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
Short summary
Short summary
Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Lena Frey, Frida A.-M. Bender, and Gunilla Svensson
Atmos. Chem. Phys., 21, 577–595, https://doi.org/10.5194/acp-21-577-2021, https://doi.org/10.5194/acp-21-577-2021, 2021
Short summary
Short summary
We investigate the vertical distribution of aerosol in the climate model NorESM1-M in five regions of marine stratocumulus clouds. We thereby analyze the total aerosol extinction to facilitate a comparison with satellite data. We find that the model underestimates aerosol extinction throughout the troposphere, especially elevated aerosol layers. Further, we perform sensitivity experiments to identify the processes most important for vertical aerosol distribution in our model.
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
Øyvind Seland, Mats Bentsen, Dirk Olivié, Thomas Toniazzo, Ada Gjermundsen, Lise Seland Graff, Jens Boldingh Debernard, Alok Kumar Gupta, Yan-Chun He, Alf Kirkevåg, Jörg Schwinger, Jerry Tjiputra, Kjetil Schanke Aas, Ingo Bethke, Yuanchao Fan, Jan Griesfeller, Alf Grini, Chuncheng Guo, Mehmet Ilicak, Inger Helene Hafsahl Karset, Oskar Landgren, Johan Liakka, Kine Onsum Moseid, Aleksi Nummelin, Clemens Spensberger, Hui Tang, Zhongshi Zhang, Christoph Heinze, Trond Iversen, and Michael Schulz
Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, https://doi.org/10.5194/gmd-13-6165-2020, 2020
Short summary
Short summary
The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. The temperature and precipitation patterns has improved compared to NorESM1. The model reaches present-day warming levels to within 0.2 °C of observed temperature but with a delayed warming during the late 20th century. Under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the warming in the period of 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.1, and 3.9 K.
Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Simon P. Alexander, John J. Cassano, Sally Garrett, Jamie Halla, Sean Hartery, Mike J. Harvey, Simon Parsons, Graeme Plank, Vidya Varma, and Jonny Williams
Atmos. Chem. Phys., 20, 6607–6630, https://doi.org/10.5194/acp-20-6607-2020, https://doi.org/10.5194/acp-20-6607-2020, 2020
Short summary
Short summary
We evaluate clouds over the Southern Ocean in the climate model HadGEM3 and reanalysis MERRA-2 using ship-based ceilometer and radiosonde observations. We find the models underestimate cloud cover by 18–25 %, with clouds below 2 km dominant in reality but lacking in the models. We find a strong link between clouds, atmospheric stability and sea surface temperature in observations but not in the models, implying that sub-grid processes do not generate enough cloud in response to these conditions.
Jerry F. Tjiputra, Jörg Schwinger, Mats Bentsen, Anne L. Morée, Shuang Gao, Ingo Bethke, Christoph Heinze, Nadine Goris, Alok Gupta, Yan-Chun He, Dirk Olivié, Øyvind Seland, and Michael Schulz
Geosci. Model Dev., 13, 2393–2431, https://doi.org/10.5194/gmd-13-2393-2020, https://doi.org/10.5194/gmd-13-2393-2020, 2020
Short summary
Short summary
Ocean biogeochemistry plays an important role in determining the atmospheric carbon dioxide concentration. Earth system models, which are regularly used to study and project future climate change, generally include an ocean biogeochemistry component. Prior to their application, such models are rigorously validated against real-world observations. In this study, we evaluate the ability of the ocean biogeochemistry in the Norwegian Earth System Model version 2 to simulate various datasets.
Anna Possner, Ryan Eastman, Frida Bender, and Franziska Glassmeier
Atmos. Chem. Phys., 20, 3609–3621, https://doi.org/10.5194/acp-20-3609-2020, https://doi.org/10.5194/acp-20-3609-2020, 2020
Short summary
Short summary
Cloud water content and the number of droplets inside clouds covary with boundary layer depth. This covariation may amplify the change in water content due to a change in droplet number inferred from long-term observations. Taking this into account shows that the change in water content for increased droplet number in observations and high-resolution simulations agrees in shallow boundary layers. Meanwhile, deep boundary layers are under-sampled in process-scale simulations and observations.
Laura E. Revell, Stefanie Kremser, Sean Hartery, Mike Harvey, Jane P. Mulcahy, Jonny Williams, Olaf Morgenstern, Adrian J. McDonald, Vidya Varma, Leroy Bird, and Alex Schuddeboom
Atmos. Chem. Phys., 19, 15447–15466, https://doi.org/10.5194/acp-19-15447-2019, https://doi.org/10.5194/acp-19-15447-2019, 2019
Short summary
Short summary
Aerosols over the Southern Ocean consist primarily of sea salt and sulfate, yet are seasonally biased in our model. We test three sulfate chemistry schemes to investigate DMS oxidation, which forms sulfate aerosol. Simulated cloud droplet number concentrations improve using more complex sulfate chemistry. We also show that a new sea spray aerosol source function, developed from measurements made on a recent Southern Ocean research voyage, improves the model's simulation of aerosol optical depth.
Lise S. Graff, Trond Iversen, Ingo Bethke, Jens B. Debernard, Øyvind Seland, Mats Bentsen, Alf Kirkevåg, Camille Li, and Dirk J. L. Olivié
Earth Syst. Dynam., 10, 569–598, https://doi.org/10.5194/esd-10-569-2019, https://doi.org/10.5194/esd-10-569-2019, 2019
Short summary
Short summary
Differences between a 1.5 and a 2.0 °C warmer global climate than 1850 conditions are discussed based on a suite of global atmosphere-only, fully coupled, and slab-ocean runs with the Norwegian Earth System Model. Responses, such as the Arctic amplification of global warming, are stronger with the fully coupled and slab-ocean configurations. While ice-free Arctic summers are rare under 1.5 °C warming in the slab-ocean runs, they are estimated to occur 18 % of the time under 2.0 °C warming.
Michael Boy, Erik S. Thomson, Juan-C. Acosta Navarro, Olafur Arnalds, Ekaterina Batchvarova, Jaana Bäck, Frank Berninger, Merete Bilde, Zoé Brasseur, Pavla Dagsson-Waldhauserova, Dimitri Castarède, Maryam Dalirian, Gerrit de Leeuw, Monika Dragosics, Ella-Maria Duplissy, Jonathan Duplissy, Annica M. L. Ekman, Keyan Fang, Jean-Charles Gallet, Marianne Glasius, Sven-Erik Gryning, Henrik Grythe, Hans-Christen Hansson, Margareta Hansson, Elisabeth Isaksson, Trond Iversen, Ingibjorg Jonsdottir, Ville Kasurinen, Alf Kirkevåg, Atte Korhola, Radovan Krejci, Jon Egill Kristjansson, Hanna K. Lappalainen, Antti Lauri, Matti Leppäranta, Heikki Lihavainen, Risto Makkonen, Andreas Massling, Outi Meinander, E. Douglas Nilsson, Haraldur Olafsson, Jan B. C. Pettersson, Nønne L. Prisle, Ilona Riipinen, Pontus Roldin, Meri Ruppel, Matthew Salter, Maria Sand, Øyvind Seland, Heikki Seppä, Henrik Skov, Joana Soares, Andreas Stohl, Johan Ström, Jonas Svensson, Erik Swietlicki, Ksenia Tabakova, Throstur Thorsteinsson, Aki Virkkula, Gesa A. Weyhenmeyer, Yusheng Wu, Paul Zieger, and Markku Kulmala
Atmos. Chem. Phys., 19, 2015–2061, https://doi.org/10.5194/acp-19-2015-2019, https://doi.org/10.5194/acp-19-2015-2019, 2019
Short summary
Short summary
The Nordic Centre of Excellence CRAICC (Cryosphere–Atmosphere Interactions in a Changing Arctic Climate), funded by NordForsk in the years 2011–2016, is the largest joint Nordic research and innovation initiative to date and aimed to strengthen research and innovation regarding climate change issues in the Nordic region. The paper presents an overview of the main scientific topics investigated and provides a state-of-the-art comprehensive summary of what has been achieved in CRAICC.
Ben Jolly, Peter Kuma, Adrian McDonald, and Simon Parsons
Atmos. Chem. Phys., 18, 9723–9739, https://doi.org/10.5194/acp-18-9723-2018, https://doi.org/10.5194/acp-18-9723-2018, 2018
Short summary
Short summary
Clouds in the Ross Sea and Ross Ice Shelf regions are examined using a combination of satellite observations from the CloudSat and CALIPSO satellite datasets. We show that previous studies may have included an artefact at high altitudes which under-estimated cloud occurrence. We also find that the meteorological regime is a stronger control of cloud occurrence, cloud type and cloud top than season over this region, though season is a strong control on the phase of cloud.
Quentin Bourgeois, Annica M. L. Ekman, Jean-Baptiste Renard, Radovan Krejci, Abhay Devasthale, Frida A.-M. Bender, Ilona Riipinen, Gwenaël Berthet, and Jason L. Tackett
Atmos. Chem. Phys., 18, 7709–7720, https://doi.org/10.5194/acp-18-7709-2018, https://doi.org/10.5194/acp-18-7709-2018, 2018
Short summary
Short summary
The altitude of aerosols is crucial as they can impact cloud formation and radiation. In this study, satellite observations have been used to characterize the global aerosol optical depth (AOD) in the boundary layer and the free troposphere. The free troposphere contributes 39 % to the global AOD during daytime. Overall, the results have implications for the description of budgets, sources, sinks and transport of aerosol particles as presently described in the atmospheric model.
Daniel T. McCoy, Paul R. Field, Anja Schmidt, Daniel P. Grosvenor, Frida A.-M. Bender, Ben J. Shipway, Adrian A. Hill, Jonathan M. Wilkinson, and Gregory S. Elsaesser
Atmos. Chem. Phys., 18, 5821–5846, https://doi.org/10.5194/acp-18-5821-2018, https://doi.org/10.5194/acp-18-5821-2018, 2018
Short summary
Short summary
Here we use a combination of global convection-permitting models, satellite observations and the Holuhraun volcanic eruption to demonstrate that aerosol enhances the cloud liquid content and brightness of midlatitude cyclones. This is important because the strength of anthropogenic radiative forcing is uncertain, leading to uncertainty in the climate sensitivity consistent with observed temperature record.
Daniel T. McCoy, Frida A.-M. Bender, Daniel P. Grosvenor, Johannes K. Mohrmann, Dennis L. Hartmann, Robert Wood, and Paul R. Field
Atmos. Chem. Phys., 18, 2035–2047, https://doi.org/10.5194/acp-18-2035-2018, https://doi.org/10.5194/acp-18-2035-2018, 2018
Short summary
Short summary
The interaction between clouds and aerosols represents the largest source of uncertainty in the anthropogenic radiative forcing. Cloud droplet number concentration (CDNC) is the state variable that moderates the interaction between aerosol and clouds. Here we show that CDNC decreases off the coasts of East Asia and North America due to controls on emissions. We support this analysis through an examination of volcanism in Hawaii and Vanuatu.
Fraser Dennison, Adrian McDonald, and Olaf Morgenstern
Atmos. Chem. Phys., 17, 14075–14084, https://doi.org/10.5194/acp-17-14075-2017, https://doi.org/10.5194/acp-17-14075-2017, 2017
Short summary
Short summary
The Antarctic ozone is not centred directly over the pole. In this research we examine how the position and shape of the ozone hole changes using a chemistry–climate model. As ozone becomes increasingly depleted during the late 20th century the centre of the ozone hole moves toward the west and becomes more circular. As the ozone hole recovers over the course of the 21st century the ozone hole moves back towards the east.
Maria Sand, Bjørn H. Samset, Yves Balkanski, Susanne Bauer, Nicolas Bellouin, Terje K. Berntsen, Huisheng Bian, Mian Chin, Thomas Diehl, Richard Easter, Steven J. Ghan, Trond Iversen, Alf Kirkevåg, Jean-François Lamarque, Guangxing Lin, Xiaohong Liu, Gan Luo, Gunnar Myhre, Twan van Noije, Joyce E. Penner, Michael Schulz, Øyvind Seland, Ragnhild B. Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Fangqun Yu, Kai Zhang, and Hua Zhang
Atmos. Chem. Phys., 17, 12197–12218, https://doi.org/10.5194/acp-17-12197-2017, https://doi.org/10.5194/acp-17-12197-2017, 2017
Short summary
Short summary
The role of aerosols in the changing polar climate is not well understood and the aerosols are poorly constrained in the models. In this study we have compared output from 16 different aerosol models with available observations at both poles. We show that the model median is representative of the observations, but the model spread is large. The Arctic direct aerosol radiative effect over the industrial area is positive during spring due to black carbon and negative during summer due to sulfate.
Lena Frey, Frida A.-M. Bender, and Gunilla Svensson
Atmos. Chem. Phys., 17, 9145–9162, https://doi.org/10.5194/acp-17-9145-2017, https://doi.org/10.5194/acp-17-9145-2017, 2017
Short summary
Short summary
In this study, the cloud albedo effect in climate models is investigated, separating the influence of anthropogenic sulfate and non-sulfate aerosols. Cloud albedo changes induced by added anthropogenic aerosols are found to be determined by changes in the cloud water content rather than model sensitivity to monthly aerosol variations. The results also indicate that the background aerosol is the main driver for a cloud brightening effect on the month-to-month scale.
Ethan R. Dale, Adrian J. McDonald, Jack H. J. Coggins, and Wolfgang Rack
The Cryosphere, 11, 267–280, https://doi.org/10.5194/tc-11-267-2017, https://doi.org/10.5194/tc-11-267-2017, 2017
Short summary
Short summary
This work studies the affects of strong winds on sea ice within the Ross Sea polynya. We compare both automatic weather station (AWS) and reanalysis wind data with sea ice concentration (SIC) measurements based on satellite images. Due to its low resolution, the reanalysis data were unable to reproduce several relationships found between the AWS and SIC data. We find that the strongest third of wind speeds had the most significant affect on SIC and resulting sea ice production.
Leon S. Friedrich, Adrian J. McDonald, Gregory E. Bodeker, Kathy E. Cooper, Jared Lewis, and Alexander J. Paterson
Atmos. Chem. Phys., 17, 855–866, https://doi.org/10.5194/acp-17-855-2017, https://doi.org/10.5194/acp-17-855-2017, 2017
Short summary
Short summary
Information from long-duration balloons flying in the Southern Hemisphere stratosphere during 2014 as part of X Project Loon are used to assess the quality of a number of different reanalyses. This work assesses the potential of the X Project Loon observations to validate outputs from the reanalysis models. In particular, we examined how the model winds compared with those derived from the balloon GPS information. We also examined simulated trajectories compared with the true trajectories.
Massimo Cassiani, Andreas Stohl, Dirk Olivié, Øyvind Seland, Ingo Bethke, Ignacio Pisso, and Trond Iversen
Geosci. Model Dev., 9, 4029–4048, https://doi.org/10.5194/gmd-9-4029-2016, https://doi.org/10.5194/gmd-9-4029-2016, 2016
Short summary
Short summary
FLEXPART is a community model used by many scientists. Here, an alternative FLEXPART model version has been developed, tailored to use with the output data generated by the Norwegian Earth System Model (NorESM1-M). The model provides an advanced tool to analyse and diagnose atmospheric transport properties of the climate model NorESM. To validate the model, several tests were performed that offered the possibility to investigate some aspects of offline global dispersion modelling.
Kristina Pistone, Puppala S. Praveen, Rick M. Thomas, Veerabhadran Ramanathan, Eric M. Wilcox, and Frida A.-M. Bender
Atmos. Chem. Phys., 16, 5203–5227, https://doi.org/10.5194/acp-16-5203-2016, https://doi.org/10.5194/acp-16-5203-2016, 2016
Short summary
Short summary
A recent field campaign (CARDEX) in the northern Indian Ocean concurrently measured cloud and aerosol properties and atmospheric structure and dynamics from a ground-based observatory and unmanned aerial vehicles (UAVs). These new measurements show a correlation between highly polluted conditions and increased cloud water content, concurrent with higher atmospheric temperature and humidity. Such correlations may be of interest in future studies of the effects of pollution on cloud properties.
Zak Kipling, Philip Stier, Colin E. Johnson, Graham W. Mann, Nicolas Bellouin, Susanne E. Bauer, Tommi Bergman, Mian Chin, Thomas Diehl, Steven J. Ghan, Trond Iversen, Alf Kirkevåg, Harri Kokkola, Xiaohong Liu, Gan Luo, Twan van Noije, Kirsty J. Pringle, Knut von Salzen, Michael Schulz, Øyvind Seland, Ragnhild B. Skeie, Toshihiko Takemura, Kostas Tsigaridis, and Kai Zhang
Atmos. Chem. Phys., 16, 2221–2241, https://doi.org/10.5194/acp-16-2221-2016, https://doi.org/10.5194/acp-16-2221-2016, 2016
Short summary
Short summary
The vertical distribution of atmospheric aerosol is an important factor in its effects on climate. In this study we use a sophisticated model of the many interacting processes affecting aerosol in the atmosphere to show that the vertical distribution is typically dominated by only a few of these processes. Constraining these physical processes may help to reduce the large differences between models. However, the important processes are not always the same for different types of aerosol.
F. Höpner, F. A.-M. Bender, A. M. L. Ekman, P. S. Praveen, C. Bosch, J. A. Ogren, A. Andersson, Ö. Gustafsson, and V. Ramanathan
Atmos. Chem. Phys., 16, 1045–1064, https://doi.org/10.5194/acp-16-1045-2016, https://doi.org/10.5194/acp-16-1045-2016, 2016
Short summary
Short summary
The paper presents aerosol properties measured during the Cloud Aerosol Radiative Forcing Experiment (CARDEX) on the Maldives Islands in winter 2012. The vertical distribution of absorbing aerosol which is very relevant to the radiative forcing in that region, is investigated. A method for determining particle absorption and equivalent black carbon concentration from lidar extinction coefficients, characteristic single scattering albedo and mass absorption efficiency, is presented and evaluated.
A. Stohl, B. Aamaas, M. Amann, L. H. Baker, N. Bellouin, T. K. Berntsen, O. Boucher, R. Cherian, W. Collins, N. Daskalakis, M. Dusinska, S. Eckhardt, J. S. Fuglestvedt, M. Harju, C. Heyes, Ø. Hodnebrog, J. Hao, U. Im, M. Kanakidou, Z. Klimont, K. Kupiainen, K. S. Law, M. T. Lund, R. Maas, C. R. MacIntosh, G. Myhre, S. Myriokefalitakis, D. Olivié, J. Quaas, B. Quennehen, J.-C. Raut, S. T. Rumbold, B. H. Samset, M. Schulz, Ø. Seland, K. P. Shine, R. B. Skeie, S. Wang, K. E. Yttri, and T. Zhu
Atmos. Chem. Phys., 15, 10529–10566, https://doi.org/10.5194/acp-15-10529-2015, https://doi.org/10.5194/acp-15-10529-2015, 2015
Short summary
Short summary
This paper presents a summary of the findings of the ECLIPSE EU project. The project has investigated the climate and air quality impacts of short-lived climate pollutants (especially methane, ozone, aerosols) and has designed a global mitigation strategy that maximizes co-benefits between air quality and climate policy. Transient climate model simulations allowed quantifying the impacts on temperature (e.g., reduction in global warming by 0.22K for the decade 2041-2050) and precipitation.
B. H. Samset, G. Myhre, A. Herber, Y. Kondo, S.-M. Li, N. Moteki, M. Koike, N. Oshima, J. P. Schwarz, Y. Balkanski, S. E. Bauer, N. Bellouin, T. K. Berntsen, H. Bian, M. Chin, T. Diehl, R. C. Easter, S. J. Ghan, T. Iversen, A. Kirkevåg, J.-F. Lamarque, G. Lin, X. Liu, J. E. Penner, M. Schulz, Ø. Seland, R. B. Skeie, P. Stier, T. Takemura, K. Tsigaridis, and K. Zhang
Atmos. Chem. Phys., 14, 12465–12477, https://doi.org/10.5194/acp-14-12465-2014, https://doi.org/10.5194/acp-14-12465-2014, 2014
Short summary
Short summary
Far from black carbon (BC) emission sources, present climate models are unable to reproduce flight measurements. By comparing recent models with data, we find that the atmospheric lifetime of BC may be overestimated in models. By adjusting modeled BC concentrations to measurements in remote regions - over oceans and at high altitudes - we arrive at a reduced estimate for BC radiative forcing over the industrial era.
K. Tsigaridis, N. Daskalakis, M. Kanakidou, P. J. Adams, P. Artaxo, R. Bahadur, Y. Balkanski, S. E. Bauer, N. Bellouin, A. Benedetti, T. Bergman, T. K. Berntsen, J. P. Beukes, H. Bian, K. S. Carslaw, M. Chin, G. Curci, T. Diehl, R. C. Easter, S. J. Ghan, S. L. Gong, A. Hodzic, C. R. Hoyle, T. Iversen, S. Jathar, J. L. Jimenez, J. W. Kaiser, A. Kirkevåg, D. Koch, H. Kokkola, Y. H Lee, G. Lin, X. Liu, G. Luo, X. Ma, G. W. Mann, N. Mihalopoulos, J.-J. Morcrette, J.-F. Müller, G. Myhre, S. Myriokefalitakis, N. L. Ng, D. O'Donnell, J. E. Penner, L. Pozzoli, K. J. Pringle, L. M. Russell, M. Schulz, J. Sciare, Ø. Seland, D. T. Shindell, S. Sillman, R. B. Skeie, D. Spracklen, T. Stavrakou, S. D. Steenrod, T. Takemura, P. Tiitta, S. Tilmes, H. Tost, T. van Noije, P. G. van Zyl, K. von Salzen, F. Yu, Z. Wang, Z. Wang, R. A. Zaveri, H. Zhang, K. Zhang, Q. Zhang, and X. Zhang
Atmos. Chem. Phys., 14, 10845–10895, https://doi.org/10.5194/acp-14-10845-2014, https://doi.org/10.5194/acp-14-10845-2014, 2014
R. Makkonen, Ø. Seland, A. Kirkevåg, T. Iversen, and J. E. Kristjánsson
Atmos. Chem. Phys., 14, 5127–5152, https://doi.org/10.5194/acp-14-5127-2014, https://doi.org/10.5194/acp-14-5127-2014, 2014
C. Jiao, M. G. Flanner, Y. Balkanski, S. E. Bauer, N. Bellouin, T. K. Berntsen, H. Bian, K. S. Carslaw, M. Chin, N. De Luca, T. Diehl, S. J. Ghan, T. Iversen, A. Kirkevåg, D. Koch, X. Liu, G. W. Mann, J. E. Penner, G. Pitari, M. Schulz, Ø. Seland, R. B. Skeie, S. D. Steenrod, P. Stier, T. Takemura, K. Tsigaridis, T. van Noije, Y. Yun, and K. Zhang
Atmos. Chem. Phys., 14, 2399–2417, https://doi.org/10.5194/acp-14-2399-2014, https://doi.org/10.5194/acp-14-2399-2014, 2014
M. Bentsen, I. Bethke, J. B. Debernard, T. Iversen, A. Kirkevåg, Ø. Seland, H. Drange, C. Roelandt, I. A. Seierstad, C. Hoose, and J. E. Kristjánsson
Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, https://doi.org/10.5194/gmd-6-687-2013, 2013
T. Iversen, M. Bentsen, I. Bethke, J. B. Debernard, A. Kirkevåg, Ø. Seland, H. Drange, J. E. Kristjansson, I. Medhaug, M. Sand, and I. A. Seierstad
Geosci. Model Dev., 6, 389–415, https://doi.org/10.5194/gmd-6-389-2013, https://doi.org/10.5194/gmd-6-389-2013, 2013
P. E. Huck, G. E. Bodeker, S. Kremser, A. J. McDonald, M. Rex, and H. Struthers
Atmos. Chem. Phys., 13, 3237–3243, https://doi.org/10.5194/acp-13-3237-2013, https://doi.org/10.5194/acp-13-3237-2013, 2013
J. F. Tjiputra, C. Roelandt, M. Bentsen, D. M. Lawrence, T. Lorentzen, J. Schwinger, Ø. Seland, and C. Heinze
Geosci. Model Dev., 6, 301–325, https://doi.org/10.5194/gmd-6-301-2013, https://doi.org/10.5194/gmd-6-301-2013, 2013
B. H. Samset, G. Myhre, M. Schulz, Y. Balkanski, S. Bauer, T. K. Berntsen, H. Bian, N. Bellouin, T. Diehl, R. C. Easter, S. J. Ghan, T. Iversen, S. Kinne, A. Kirkevåg, J.-F. Lamarque, G. Lin, X. Liu, J. E. Penner, Ø. Seland, R. B. Skeie, P. Stier, T. Takemura, K. Tsigaridis, and K. Zhang
Atmos. Chem. Phys., 13, 2423–2434, https://doi.org/10.5194/acp-13-2423-2013, https://doi.org/10.5194/acp-13-2423-2013, 2013
A. Kirkevåg, T. Iversen, Ø. Seland, C. Hoose, J. E. Kristjánsson, H. Struthers, A. M. L. Ekman, S. Ghan, J. Griesfeller, E. D. Nilsson, and M. Schulz
Geosci. Model Dev., 6, 207–244, https://doi.org/10.5194/gmd-6-207-2013, https://doi.org/10.5194/gmd-6-207-2013, 2013
M. Sand, T. K. Berntsen, J. E. Kay, J. F. Lamarque, Ø. Seland, and A. Kirkevåg
Atmos. Chem. Phys., 13, 211–224, https://doi.org/10.5194/acp-13-211-2013, https://doi.org/10.5194/acp-13-211-2013, 2013
Related subject area
Subject: Clouds and Precipitation | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Above-cloud concentrations of cloud condensation nuclei help to sustain some Arctic low-level clouds
Contrail formation on ambient aerosol particles for aircraft with hydrogen combustion: a box model trajectory study
Effects of intermittent aerosol forcing on the stratocumulus-to-cumulus transition
Cloud properties and their projected changes in CMIP models with low to high climate sensitivity
Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 2: The imprint of the atmospheric circulation at different scales
Evaluating the Wegener-Bergeron-Findeisen process in ICON in large-eddy mode with in situ observations from the CLOUDLAB project
Impact of urban land use on mean and heavy rainfall during the Indian summer monsoon
Distribution and morphology of non-persistent and persistent contrail formation areas in ERA5
Towards a more reliable forecast of ice supersaturation: concept of a one-moment ice-cloud scheme that avoids saturation adjustment
Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts
Variability of the properties of the distribution of the relative humidity with respect to ice: Implications for contrail formation
Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model
Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 1: A process-oriented evaluation of COSMOiso simulations with EUREC4A observations
Simulating the seeder-feeder impacts on cloud ice and precipitation over the Alps
Assimilation of 3D polarimetric microphysical retrievals in a convective-scale NWP system
Sensitivity of cloud-phase distribution to cloud microphysics and thermodynamics in simulated deep convective clouds and SEVIRI retrievals
Interactions between trade-wind clouds and local forcings over the Great Barrier Reef: A case study using convection-permitting simulations
Assessing the destructiveness of tropical cyclones induced by anthropogenic aerosols in an atmosphere–ocean coupled framework
Opinion: A critical evaluation of the evidence for aerosol invigoration of deep convection
Impact of ice multiplication on the cloud electrification of a cold-season thunderstorm: a numerical case study
Aerosol-Induced Closure of Marine Cloud Cells: Enhanced Effects in the Presence of Precipitation
Historical (1960–2014) lightning and LNOx trends and their controlling factors in a chemistry–climate model
The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations
Evaluation of hygroscopic cloud seeding in warm-rain processes by a hybrid microphysics scheme using a Weather Research and Forecasting (WRF) model: a real case study
Effects of longwave radiative cooling on advection fog over the Northwest Pacific Ocean: Observations and large eddy simulations
Radiation fog properties in two consecutive events under polluted and clean conditions in the Yangtze River Delta, China: a simulation study
A bin microphysics parcel model investigation of secondary ice formation in an idealised shallow convective cloud
Influence of atmospheric rivers and associated weather systems on precipitation in the Arctic
Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations
Interaction of microphysics and dynamics in a warm conveyor belt simulated with the ICOsahedral Nonhydrostatic (ICON) model
Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?
Large-eddy simulation of a two-layer boundary-layer cloud system from the Arctic Ocean 2018 expedition
Opposing trends of cloud coverage over land and ocean under global warming
Aerosol–cloud–radiation interaction during Saharan dust episodes: the dusty cirrus puzzle
Aerosol–cloud impacts on aerosol detrainment and rainout in shallow maritime tropical clouds
Mixed-phase direct numerical simulation: ice growth in cloud-top generating cells
Aerosol impacts on the entrainment efficiency of Arctic mixed-phase convection in a simulated air mass over open water
Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System
Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework
Cloud response to co-condensation of water and organic vapors over the boreal forest
Impact of formulations of the homogeneous nucleation rate on ice nucleation events in cirrus
Temperature and cloud condensation nuclei (CCN) sensitivity of orographic precipitation enhanced by a mixed-phase seeder–feeder mechanism: a case study for the 2015 Cumbria flood
Aerosol–precipitation elevation dependence over the central Himalayas using cloud-resolving WRF-Chem numerical modeling
A modeling study of an extreme rainfall event along the northern coast of Taiwan on 2 June 2017
Long-term upper-troposphere climatology of potential contrail occurrence over the Paris area derived from radiosonde observations
Equilibrium climate sensitivity increases with aerosol concentration due to changes in precipitation efficiency
Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?
Aerosol characteristics and polarimetric signatures for a deep convective storm over the northwestern part of Europe – modeling and observations
Evaluation of tropical water vapour from CMIP6 global climate models using the ESA CCI Water Vapour climate data records
Aerosol–stratocumulus interactions: towards a better process understanding using closures between observations and large eddy simulations
Lucas J. Sterzinger and Adele L. Igel
Atmos. Chem. Phys., 24, 3529–3540, https://doi.org/10.5194/acp-24-3529-2024, https://doi.org/10.5194/acp-24-3529-2024, 2024
Short summary
Short summary
Using idealized large eddy simulations, we find that clouds forming in the Arctic in environments with low concentrations of aerosol particles may be sustained by mixing in new particles through the cloud top. Observations show that higher concentrations of these particles regularly exist above cloud top in concentrations that are sufficient to promote this sustenance.
Andreas Bier, Simon Unterstrasser, Josef Zink, Dennis Hillenbrand, Tina Jurkat-Witschas, and Annemarie Lottermoser
Atmos. Chem. Phys., 24, 2319–2344, https://doi.org/10.5194/acp-24-2319-2024, https://doi.org/10.5194/acp-24-2319-2024, 2024
Short summary
Short summary
Using hydrogen as aviation fuel affects contrails' climate impact. We study contrail formation behind aircraft with H2 combustion. Due to the absence of soot emissions, contrail ice crystals are assumed to form only on ambient particles mixed into the plume. The ice crystal number, which strongly varies with temperature and aerosol number density, is decreased by more than 80 %–90 % compared to kerosene contrails. However H2 contrails can form at lower altitudes due to higher H2O emissions.
Prasanth Prabhakaran, Fabian Hoffmann, and Graham Feingold
Atmos. Chem. Phys., 24, 1919–1937, https://doi.org/10.5194/acp-24-1919-2024, https://doi.org/10.5194/acp-24-1919-2024, 2024
Short summary
Short summary
In this study, we explore the impact of deliberate aerosol perturbation in the northeast Pacific region using large-eddy simulations. Our results show that cloud reflectivity is sensitive to the aerosol sprayer arrangement in the pristine system, whereas in the polluted system it is largely proportional to the total number of aerosol particles injected. These insights would aid in assessing the efficiency of various aerosol injection strategies for climate intervention applications.
Lisa Bock and Axel Lauer
Atmos. Chem. Phys., 24, 1587–1605, https://doi.org/10.5194/acp-24-1587-2024, https://doi.org/10.5194/acp-24-1587-2024, 2024
Short summary
Short summary
Climate model simulations still show a large range of effective climate sensitivity (ECS) with high uncertainties. An important contribution to ECS is cloud climate feedback. We investigate the representation of cloud physical and radiative properties from Coupled Model Intercomparison Project models grouped by ECS. We compare the simulated cloud properties of today’s climate from three ECS groups and quantify how the projected changes in cloud properties and cloud radiative effects differ.
Leonie Villiger and Franziska Aemisegger
Atmos. Chem. Phys., 24, 957–976, https://doi.org/10.5194/acp-24-957-2024, https://doi.org/10.5194/acp-24-957-2024, 2024
Short summary
Short summary
Three numerical simulations performed with an isotope-enabled weather forecast model are used to investigate the cloud–circulation coupling between shallow trade-wind cumulus clouds and atmospheric circulations on different scales. It is shown that stable water isotopes near cloud base in the tropics reflect (1) the diel cycle of the atmospheric circulation, which drives the formation and dissipation of clouds, and (2) changes in the large-scale circulation over the North Atlantic.
Nadja Omanovic, Sylvaine Ferrachat, Christopher Fuchs, Jan Henneberger, Anna J. Miller, Kevin Ohneiser, Fabiola Ramelli, Patric Seifert, Robert Spirig, Huiying Zhang, and Ulrike Lohmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-3029, https://doi.org/10.5194/egusphere-2023-3029, 2024
Short summary
Short summary
We present simulations with a high-resolution numerical weather prediction model to study the growth of ice crystals in low clouds following glaciogenic seeding. We show that the simulated ice crystals grow slower than observed and do not consume as many cloud droplets as measured in the field. This may have implications for forecasting precipitation as the ice phase is crucial for precipitation in mid- and high latitudes.
Renaud Falga and Chien Wang
Atmos. Chem. Phys., 24, 631–647, https://doi.org/10.5194/acp-24-631-2024, https://doi.org/10.5194/acp-24-631-2024, 2024
Short summary
Short summary
The impact of urban land use on regional meteorology and rainfall during the Indian summer monsoon has been assessed in this study. Using a cloud-resolving model centered around Kolkata, we have shown that the urban heat island effect led to a rainfall enhancement via the amplification of convective activity, especially during the night. Furthermore, the results demonstrated that the kinetic effect of the city induced the initiation of a nighttime storm.
Kevin Wolf, Nicolas Bellouin, and Olivier Boucher
EGUsphere, https://doi.org/10.5194/egusphere-2023-3086, https://doi.org/10.5194/egusphere-2023-3086, 2024
Short summary
Short summary
The contrail formation potential and its tempo-spatial distribution are estimated for the North Atlantic flight corridor. Meteorological conditions of temperature and relative humidity are taken from the ERA5 re-analysis and IAGOS. Based on IAGOS flight tracks, crossing length, size, orientation, frequency of occurrence, and overlap of persistent contrail formation areas are determined. The presented conclusions might provide a guide for statistical flight track optimization to reduce contrails.
Dario Sperber and Klaus Gierens
Atmos. Chem. Phys., 23, 15609–15627, https://doi.org/10.5194/acp-23-15609-2023, https://doi.org/10.5194/acp-23-15609-2023, 2023
Short summary
Short summary
A significant share of aviation's climate impact is due to persistent contrails. Avoiding their creation is a step toward sustainable air transportation. For this purpose, a reliable forecast of so-called ice-supersaturated regions is needed, which then allows one to plan aircraft routes without persistent contrails. Here, we propose a method that leads to the better prediction of ice-supersaturated regions.
Blaž Gasparini, Sylvia C. Sullivan, Adam B. Sokol, Bernd Kärcher, Eric Jensen, and Dennis L. Hartmann
Atmos. Chem. Phys., 23, 15413–15444, https://doi.org/10.5194/acp-23-15413-2023, https://doi.org/10.5194/acp-23-15413-2023, 2023
Short summary
Short summary
Tropical cirrus clouds are essential for climate, but our understanding of these clouds is limited due to their dependence on a wide range of small- and large-scale climate processes. In this opinion paper, we review recent advances in the study of tropical cirrus clouds, point out remaining open questions, and suggest ways to resolve them.
Sidiki Sanogo, Olivier Boucher, Nicolas Bellouin, Audran Borella, Kevin Wolf, and Susanne Rohs
EGUsphere, https://doi.org/10.5194/egusphere-2023-2601, https://doi.org/10.5194/egusphere-2023-2601, 2023
Short summary
Short summary
Relative humidity relative to ice (RHi) is a key variable in the formation of cirrus clouds and contrails. This study shows that the properties of the probability density function of RHi differ between the tropics and higher latitudes. In link with RHi and temperature variability, aircraft are likely to produce more contrails with bioethanol and hydrogen as fuel. The impact of this fuel change decreases with decreasing pressure levels, but increases from high latitudes to the tropics.
Ulrike Proske, Sylvaine Ferrachat, and Ulrike Lohmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2783, https://doi.org/10.5194/egusphere-2023-2783, 2023
Short summary
Short summary
Climate models include treatment of aerosol particles because these influence clouds and radiation. Over time their representation has grown increasingly detailed. This complexity may hinder our understanding of model behaviour. Thus here we simplify the aerosol representation of our climate model by prescribing a mean concentration, which saves runtime and helps to discover unexpected model behaviour. We conclude that simplifications provide a new perspective for model study and development.
Leonie Villiger, Marina Dütsch, Sandrine Bony, Marie Lothon, Stephan Pfahl, Heini Wernli, Pierre-Etienne Brilouet, Patrick Chazette, Pierre Coutris, Julien Delanoë, Cyrille Flamant, Alfons Schwarzenboeck, Martin Werner, and Franziska Aemisegger
Atmos. Chem. Phys., 23, 14643–14672, https://doi.org/10.5194/acp-23-14643-2023, https://doi.org/10.5194/acp-23-14643-2023, 2023
Short summary
Short summary
This study evaluates three numerical simulations performed with an isotope-enabled weather forecast model and investigates the coupling between shallow trade-wind cumulus clouds and atmospheric circulations on different scales. We show that the simulations reproduce key characteristics of shallow trade-wind clouds as observed during the field experiment EUREC4A and that the spatial distribution of stable-water-vapour isotopes is shaped by the overturning circulation associated with these clouds.
Zane Dedekind, Ulrike Proske, Sylvaine Ferrachat, Ulrike Lohmann, and David Neubauer
EGUsphere, https://doi.org/10.5194/egusphere-2023-874, https://doi.org/10.5194/egusphere-2023-874, 2023
Short summary
Short summary
Ice particles precipitating into lower clouds from an upper cloud, the seeder-feeder process, can enhance precipitation. A numerical modeling study conducted in the Swiss Alps found that 48 % of observed clouds were overlapping, in which the seeder-feeder process occurred 10 % of these clouds. Inhibiting the seeder-feeder process reduced the surface precipitation and ice particle growth rates, which were further reduced when additional ice multiplication processes were included in the model.
Lucas Reimann, Clemens Simmer, and Silke Trömel
Atmos. Chem. Phys., 23, 14219–14237, https://doi.org/10.5194/acp-23-14219-2023, https://doi.org/10.5194/acp-23-14219-2023, 2023
Short summary
Short summary
Polarimetric radar observations were assimilated for the first time in a convective-scale numerical weather prediction system in Germany and their impact on short-term precipitation forecasts was evaluated. The assimilation was performed using microphysical retrievals of liquid and ice water content and yielded slightly improved deterministic 9 h precipitation forecasts for three intense summer precipitation cases with respect to the assimilation of radar reflectivity alone.
Cunbo Han, Corinna Hoose, Martin Stengel, Quentin Coopman, and Andrew Barrett
Atmos. Chem. Phys., 23, 14077–14095, https://doi.org/10.5194/acp-23-14077-2023, https://doi.org/10.5194/acp-23-14077-2023, 2023
Short summary
Short summary
Cloud phase has been found to significantly impact cloud thermodynamics and Earth’s radiation budget, and various factors influence it. This study investigates the sensitivity of the cloud-phase distribution to the ice-nucleating particle concentration and thermodynamics. Multiple simulation experiments were performed using the ICON model at the convection-permitting resolution of 1.2 km. Simulation results were compared to two different retrieval products based on SEVIRI measurements.
Wenhui Zhao, Yi Huang, Steven Thomas Siems, Michael James Manton, and Daniel Patrick Harrison
EGUsphere, https://doi.org/10.5194/egusphere-2023-2633, https://doi.org/10.5194/egusphere-2023-2633, 2023
Short summary
Short summary
We studied how shallow clouds and rain behave over the Great Barrier Reef (GBR) using a detailed weather model. We found that the shape of the land, especially mountains, and particles in the air play big roles in influencing these clouds. Surprisingly, the sea's temperature had a smaller effect. Our research helps us understand the GBR's climate and how various factors can influence it, where the importance of the local cloud in thermal coral bleaching has recently been identified.
Yun Lin, Yuan Wang, Jen-Shan Hsieh, Jonathan H. Jiang, Qiong Su, Lijun Zhao, Michael Lavallee, and Renyi Zhang
Atmos. Chem. Phys., 23, 13835–13852, https://doi.org/10.5194/acp-23-13835-2023, https://doi.org/10.5194/acp-23-13835-2023, 2023
Short summary
Short summary
Tropical cyclones (TCs) can cause catastrophic damage to coastal regions. We used a numerical model that explicitly simulates aerosol–cloud interaction and atmosphere–ocean coupling. We show that aerosols and ocean coupling work together to make TC storms bigger but weaker. Moreover, TCs in polluted air have more rainfall and higher sea levels, leading to more severe storm surges and flooding. Our research highlights the roles of aerosols and ocean-coupling feedbacks in TC hazard assessment.
Adam C. Varble, Adele L. Igel, Hugh Morrison, Wojciech W. Grabowski, and Zachary J. Lebo
Atmos. Chem. Phys., 23, 13791–13808, https://doi.org/10.5194/acp-23-13791-2023, https://doi.org/10.5194/acp-23-13791-2023, 2023
Short summary
Short summary
As atmospheric particles called aerosols increase in number, the number of droplets in clouds tends to increase, which has been theorized to increase storm intensity. We critically evaluate the evidence for this theory, showing that flaws and limitations of previous studies coupled with unaddressed cloud process complexities draw it into question. We provide recommendations for future observations and modeling to overcome current uncertainties.
Jing Yang, Shiye Huang, Qilin Zhang, Xiaoqin Jing, Yuting Deng, and Yubao Liu
EGUsphere, https://doi.org/10.5194/egusphere-2023-2188, https://doi.org/10.5194/egusphere-2023-2188, 2023
Short summary
Short summary
This study contributes to fill the dearth of understanding the impacts of different secondary ice production (SIP) processes on the cloud electrification in cold-season thunderstorm. The results suggest the SIP, especially the rime-splintering process and the shattering of freezing drops, have significant impacts on the charge structure of the storm. In addition, the modelled radar composite reflectivity and flash rate are improved after implementing the three SIP processes in the model.
Matthew W. Christensen, Peng Wu, Adam C. Varble, Heng Xiao, and Jerome D. Fast
EGUsphere, https://doi.org/10.5194/egusphere-2023-2416, https://doi.org/10.5194/egusphere-2023-2416, 2023
Short summary
Short summary
Clouds are essential to keep Earth cooler by reflecting sunlight back to space. We show that an increase in aerosol concentration suppresses precipitation in clouds, causing them to accumulate water and expand in a polluted environment with stronger turbulence and radiative cooling. This process enhances their reflectance by 51 %. It’s therefore prudent to account for cloud fraction changes in assessments of aerosol-cloud interactions to improve predictions of climate change.
Yanfeng He and Kengo Sudo
Atmos. Chem. Phys., 23, 13061–13085, https://doi.org/10.5194/acp-23-13061-2023, https://doi.org/10.5194/acp-23-13061-2023, 2023
Short summary
Short summary
Lightning has big social impacts. Lightning-produced NOx (LNOx) plays a vital role in atmospheric chemistry and climate. Investigating past lightning and LNOx trends can provide essential indicators of all lightning-related phenomena. Simulations show almost flat global lightning and LNOx trends during 1960–2014. Past global warming enhances the trends positively, but increases in aerosol have the opposite effect. Moreover, global lightning decreased markedly after the Pinatubo eruption.
Hannah C. Frostenberg, André Welti, Mikael Luhr, Julien Savre, Erik S. Thomson, and Luisa Ickes
Atmos. Chem. Phys., 23, 10883–10900, https://doi.org/10.5194/acp-23-10883-2023, https://doi.org/10.5194/acp-23-10883-2023, 2023
Short summary
Short summary
Observations show that ice-nucleating particle concentrations (INPCs) have a large variety and follow lognormal distributions for a given temperature. We introduce a new immersion freezing parameterization that applies this lognormal behavior. INPCs are drawn randomly from a temperature-dependent lognormal distribution. We then show that the ice content of the modeled Arctic stratocumulus cloud is highly sensitive to the probability of drawing large INPCs.
Kai-I Lin, Kao-Shen Chung, Sheng-Hsiang Wang, Li-Hsin Chen, Yu-Chieng Liou, Pay-Liam Lin, Wei-Yu Chang, Hsien-Jung Chiu, and Yi-Hui Chang
Atmos. Chem. Phys., 23, 10423–10438, https://doi.org/10.5194/acp-23-10423-2023, https://doi.org/10.5194/acp-23-10423-2023, 2023
Short summary
Short summary
This study develops a hybrid microphysics scheme to enable the complex model simulation of cloud seeding based on observational cloud condensation nuclei size distribution. Our results show that more precipitation can be developed in the scenarios seeding in the in-cloud region, and seeding over an area of tens km2 is the most efficient strategy due to the strengthening of the accretion process. Moreover, particles bigger than 0.4 μm are the main factor contributing to cloud-seeding effects.
Liu Yang, Saisai Ding, Jing-Wu Liu, and Su-Ping Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1494, https://doi.org/10.5194/egusphere-2023-1494, 2023
Short summary
Short summary
Advection fog occurs when warm and moist air moves over a cold sea surface. In this situation, the temperature of the foggy air usually drops below the sea surface temperature (SST), particularly at night. High-resolution simulations show that the cooling effect of longwave radiation from the top of the fog layer permeates through the fog, resulting in a cooling of the surface air below SST. This study emphasizes the significance of monitoring air temperature to enhance sea fog forecasting.
Naifu Shao, Chunsong Lu, Xingcan Jia, Yuan Wang, Yubin Li, Yan Yin, Bin Zhu, Tianliang Zhao, Duanyang Liu, Shengjie Niu, Shuxian Fan, Shuqi Yan, and Jingjing Lv
Atmos. Chem. Phys., 23, 9873–9890, https://doi.org/10.5194/acp-23-9873-2023, https://doi.org/10.5194/acp-23-9873-2023, 2023
Short summary
Short summary
Fog is an important meteorological phenomenon that affects visibility. Aerosols and the planetary boundary layer (PBL) play critical roles in the fog life cycle. In this study, aerosol-induced changes in fog properties become more remarkable in the second fog (Fog2) than in the first fog (Fog1). The reason is that aerosol–cloud interaction (ACI) delays Fog1 dissipation, leading to the PBL meteorological conditions being more conducive to Fog2 formation and to stronger ACI in Fog2.
Rachel L. James, Jonathan Crosier, and Paul J. Connolly
Atmos. Chem. Phys., 23, 9099–9121, https://doi.org/10.5194/acp-23-9099-2023, https://doi.org/10.5194/acp-23-9099-2023, 2023
Short summary
Short summary
Secondary ice production (SIP) may significantly enhance the ice particle concentration in mixed-phase clouds. We present a systematic modelling study of secondary ice formation in idealised shallow convective clouds for various conditions. Our results suggest that the SIP mechanism of collisions of supercooled water drops with more massive ice particles may be a significant ice formation mechanism in shallow convective clouds outside the rime-splintering temperature range (−3 to −8 °C).
Melanie Lauer, Annette Rinke, Irina Gorodetskaya, Michael Sprenger, Mario Mech, and Susanne Crewell
Atmos. Chem. Phys., 23, 8705–8726, https://doi.org/10.5194/acp-23-8705-2023, https://doi.org/10.5194/acp-23-8705-2023, 2023
Short summary
Short summary
We present a new method to analyse the influence of atmospheric rivers (ARs), cyclones, and fronts on the precipitation in the Arctic, based on two campaigns: ACLOUD (early summer 2017) and AFLUX (early spring 2019). There are differences between both campaign periods: in early summer, the precipitation is mostly related to ARs and fronts, especially when they are co-located, while in early spring, cyclones isolated from ARs and fronts contributed most to the precipitation.
Yuan Wang, Xiaojian Zheng, Xiquan Dong, Baike Xi, and Yuk L. Yung
Atmos. Chem. Phys., 23, 8591–8605, https://doi.org/10.5194/acp-23-8591-2023, https://doi.org/10.5194/acp-23-8591-2023, 2023
Short summary
Short summary
Marine boundary layer clouds remain poorly predicted in global climate models due to multiple entangled uncertainty sources. This study uses the in situ observations from a recent field campaign to constrain and evaluate cloud physics in a simplified version of a climate model. Progress and remaining issues in the cloud physics parameterizations are identified. We systematically evaluate the impacts of large-scale forcing, microphysical scheme, and aerosol concentrations on the cloud property.
Annika Oertel, Annette K. Miltenberger, Christian M. Grams, and Corinna Hoose
Atmos. Chem. Phys., 23, 8553–8581, https://doi.org/10.5194/acp-23-8553-2023, https://doi.org/10.5194/acp-23-8553-2023, 2023
Short summary
Short summary
Warm conveyor belts (WCBs) are cloud- and precipitation-producing airstreams in extratropical cyclones that are important for the large-scale flow and cloud radiative forcing. We analyze cloud formation processes during WCB ascent in a two-moment microphysics scheme. Quantification of individual diabatic heating rates shows the importance of condensation, vapor deposition, rain evaporation, melting, and cloud-top radiative cooling for total heating and WCB-related potential vorticity structure.
Colin Tully, David Neubauer, Diego Villanueva, and Ulrike Lohmann
Atmos. Chem. Phys., 23, 7673–7698, https://doi.org/10.5194/acp-23-7673-2023, https://doi.org/10.5194/acp-23-7673-2023, 2023
Short summary
Short summary
This study details the first attempt with a GCM to simulate a fully prognostic aerosol species specifically for cirrus climate intervention. The new approach is in line with the real-world delivery mechanism via aircraft. However, to achieve an appreciable signal from seeding, smaller particles were needed, and their mass emissions needed to be scaled by at least a factor of 100. These biases contributed to either overseeding or small and insignificant effects in response to seeding cirrus.
Ines Bulatovic, Julien Savre, Michael Tjernström, Caroline Leck, and Annica M. L. Ekman
Atmos. Chem. Phys., 23, 7033–7055, https://doi.org/10.5194/acp-23-7033-2023, https://doi.org/10.5194/acp-23-7033-2023, 2023
Short summary
Short summary
We use numerical modeling with detailed cloud microphysics to investigate a low-altitude cloud system consisting of two cloud layers – a type of cloud situation which was commonly observed during the summer of 2018 in the central Arctic (north of 80° N). The model generally reproduces the observed cloud layers and the thermodynamic structure of the lower atmosphere well. The cloud system is maintained unless there are low aerosol number concentrations or high large-scale wind speeds.
Huan Liu, Ilan Koren, Orit Altaratz, and Mickaël D. Chekroun
Atmos. Chem. Phys., 23, 6559–6569, https://doi.org/10.5194/acp-23-6559-2023, https://doi.org/10.5194/acp-23-6559-2023, 2023
Short summary
Short summary
Clouds' responses to global warming contribute the largest uncertainty in climate prediction. Here, we analyze 42 years of global cloud cover in reanalysis data and show a decreasing trend over most continents and an increasing trend over the tropical and subtropical oceans. A reduction in near-surface relative humidity can explain the decreasing trend in cloud cover over land. Our results suggest potential stress on the terrestrial water cycle, associated with global warming.
Axel Seifert, Vanessa Bachmann, Florian Filipitsch, Jochen Förstner, Christian M. Grams, Gholam Ali Hoshyaripour, Julian Quinting, Anika Rohde, Heike Vogel, Annette Wagner, and Bernhard Vogel
Atmos. Chem. Phys., 23, 6409–6430, https://doi.org/10.5194/acp-23-6409-2023, https://doi.org/10.5194/acp-23-6409-2023, 2023
Short summary
Short summary
We investigate how mineral dust can lead to the formation of cirrus clouds. Dusty cirrus clouds lead to a reduction in solar radiation at the surface and, hence, a reduced photovoltaic power generation. Current weather prediction systems are not able to predict this interaction between mineral dust and cirrus clouds. We have developed a new physical description of the formation of dusty cirrus clouds. Overall we can show a considerable improvement in the forecast quality of clouds and radiation.
Gabrielle R. Leung, Stephen M. Saleeby, G. Alexander Sokolowsky, Sean W. Freeman, and Susan C. van den Heever
Atmos. Chem. Phys., 23, 5263–5278, https://doi.org/10.5194/acp-23-5263-2023, https://doi.org/10.5194/acp-23-5263-2023, 2023
Short summary
Short summary
This study uses a suite of high-resolution simulations to explore how the concentration and type of aerosol particles impact shallow tropical clouds and the overall aerosol budget. Under more-polluted conditions, there are more aerosol particles present, but we also find that clouds are less able to remove those aerosol particles via rainout. Instead, those aerosol particles are more likely to be detrained aloft and remain in the atmosphere for further aerosol–cloud interactions.
Sisi Chen, Lulin Xue, Sarah Tessendorf, Kyoko Ikeda, Courtney Weeks, Roy Rasmussen, Melvin Kunkel, Derek Blestrud, Shaun Parkinson, Melinda Meadows, and Nick Dawson
Atmos. Chem. Phys., 23, 5217–5231, https://doi.org/10.5194/acp-23-5217-2023, https://doi.org/10.5194/acp-23-5217-2023, 2023
Short summary
Short summary
The possible mechanism of effective ice growth in the cloud-top generating cells in winter orographic clouds is explored using a newly developed ultra-high-resolution cloud microphysics model. Simulations demonstrate that a high availability of moisture and liquid water is critical for producing large ice particles. Fluctuations in temperature and moisture down to millimeter scales due to cloud turbulence can substantially affect the growth history of the individual cloud particles.
Jan Chylik, Dmitry Chechin, Regis Dupuy, Birte S. Kulla, Christof Lüpkes, Stephan Mertes, Mario Mech, and Roel A. J. Neggers
Atmos. Chem. Phys., 23, 4903–4929, https://doi.org/10.5194/acp-23-4903-2023, https://doi.org/10.5194/acp-23-4903-2023, 2023
Short summary
Short summary
Arctic low-level clouds play an important role in the ongoing warming of the Arctic. Unfortunately, these clouds are not properly represented in weather forecast and climate models. This study tries to cover this gap by focusing on clouds over open water during the spring, observed by research aircraft near Svalbard. The study combines the high-resolution model with sets of observational data. The results show the importance of processes that involve both ice and the liquid water in the clouds.
Gillian Young McCusker, Jutta Vüllers, Peggy Achtert, Paul Field, Jonathan J. Day, Richard Forbes, Ruth Price, Ewan O'Connor, Michael Tjernström, John Prytherch, Ryan Neely III, and Ian M. Brooks
Atmos. Chem. Phys., 23, 4819–4847, https://doi.org/10.5194/acp-23-4819-2023, https://doi.org/10.5194/acp-23-4819-2023, 2023
Short summary
Short summary
In this study, we show that recent versions of two atmospheric models – the Unified Model and Integrated Forecasting System – overestimate Arctic cloud fraction within the lower troposphere by comparison with recent remote-sensing measurements made during the Arctic Ocean 2018 expedition. The overabundance of cloud is interlinked with the modelled thermodynamic structure, with strong negative temperature biases coincident with these overestimated cloud layers.
Matthew W. Christensen, Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast
Atmos. Chem. Phys., 23, 2789–2812, https://doi.org/10.5194/acp-23-2789-2023, https://doi.org/10.5194/acp-23-2789-2023, 2023
Short summary
Short summary
An increase in aerosol concentration (tiny airborne particles) is shown to suppress rainfall and increase the abundance of droplets in clouds passing over Graciosa Island in the Azores. Cloud drops remain affected by aerosol for several days across thousands of kilometers in satellite data. Simulations from an Earth system model show good agreement, but differences in the amount of cloud water and its extent remain despite modifications to model parameters that control the warm-rain process.
Liine Heikkinen, Daniel G. Partridge, Wei Huang, Sara Blichner, Rahul Ranjan, Emanuele Tovazzi, Tuukka Petäjä, Claudia Mohr, and Ilona Riipinen
EGUsphere, https://doi.org/10.5194/egusphere-2023-164, https://doi.org/10.5194/egusphere-2023-164, 2023
Short summary
Short summary
The organic vapor condensation with water vapor (co-condensation) is modeled in this work over the boreal forest environment because the forest air is rich in naturally emitted organic vapors. The simulations show that the number of cloud droplets can enhance by 20 % if the co-condensation process is considered. The enhancements are particularly high if the air contains small, naturally produced particles. Such conditions are most frequently met in Spring in the boreal forest.
Peter Spichtinger, Patrik Marschalik, and Manuel Baumgartner
Atmos. Chem. Phys., 23, 2035–2060, https://doi.org/10.5194/acp-23-2035-2023, https://doi.org/10.5194/acp-23-2035-2023, 2023
Short summary
Short summary
We investigate the impact of the homogeneous nucleation rate on nucleation events in cirrus. As long as the slope of the rate is represented sufficiently well, the resulting ice crystal number concentrations are not crucially affected. Even a change in the prefactor over orders of magnitude does not change the results. However, the maximum supersaturation during nucleation events shows strong changes. This quantity should be used for diagnostics instead of the popular nucleation threshold.
Julia Thomas, Andrew Barrett, and Corinna Hoose
Atmos. Chem. Phys., 23, 1987–2002, https://doi.org/10.5194/acp-23-1987-2023, https://doi.org/10.5194/acp-23-1987-2023, 2023
Short summary
Short summary
We study the sensitivity of rain formation processes during a heavy-rainfall event over mountains to changes in temperature and pollution. Total rainfall increases by 2 % K−1, and a 6 % K−1 increase is found at the highest altitudes, caused by a mixed-phase seeder–feeder mechanism (frozen cloud particles melt and grow further as they fall through a liquid cloud layer). In a cleaner atmosphere this process is enhanced. Thus the risk of severe rainfall in mountains may increase in the future.
Pramod Adhikari and John F. Mejia
Atmos. Chem. Phys., 23, 1019–1042, https://doi.org/10.5194/acp-23-1019-2023, https://doi.org/10.5194/acp-23-1019-2023, 2023
Short summary
Short summary
We used an atmospheric model to assess the impact of aerosols through radiation and cloud interaction on elevation-dependent precipitation and surface temperature over the central Himalayan region. Results showed contrasting altitudinal precipitation responses to the increased aerosol concentration, which can significantly impact the hydroclimate of the central Himalayas, increasing the risk for extreme events and influencing the regional supply of water resources.
Chung-Chieh Wang, Ting-Yu Yeh, Chih-Sheng Chang, Ming-Siang Li, Kazuhisa Tsuboki, and Ching-Hwang Liu
Atmos. Chem. Phys., 23, 501–521, https://doi.org/10.5194/acp-23-501-2023, https://doi.org/10.5194/acp-23-501-2023, 2023
Short summary
Short summary
The extreme rainfall event (645 mm in 24 h) at the northern coast of Taiwan on 2 June 2017 is studied using a cloud model. Two 1 km experiments with peak amounts of 541 and 400 mm are compared to isolate the reasons for such a difference. It is found that the frontal rainband remains fixed in location for a longer period in the former run due to a low disturbance that acts to focus the near-surface convergence. Therefore, the rainfall is more concentrated and there is a higher total amount.
Kevin Wolf, Nicolas Bellouin, and Olivier Boucher
Atmos. Chem. Phys., 23, 287–309, https://doi.org/10.5194/acp-23-287-2023, https://doi.org/10.5194/acp-23-287-2023, 2023
Short summary
Short summary
Recent studies estimate the radiative impact of contrails to be similar to or larger than that of emitted CO2; thus, contrail mitigation might be an opportunity to reduce the climate effects of aviation. A radiosonde data set is analyzed in terms of the vertical distribution of potential contrails, contrail mitigation by flight altitude changes, and linkages with the tropopause and jet stream. The effect of prospective jet engine developments and alternative fuels are estimated.
Guy Dagan
Atmos. Chem. Phys., 22, 15767–15775, https://doi.org/10.5194/acp-22-15767-2022, https://doi.org/10.5194/acp-22-15767-2022, 2022
Short summary
Short summary
Using idealized simulations we demonstrate that the equilibrium climate sensitivity (ECS), i.e. the increase in surface temperature under equilibrium conditions due to doubling of the CO2 concentration, increases with the aerosol concentration. The ECS increase is explained by a faster increase in precipitation efficiency with warming under high aerosol concentrations, which more efficiently depletes the water from the cloud and thus is manifested as an increase in the cloud feedback parameter.
Sonya L. Fiddes, Alain Protat, Marc D. Mallet, Simon P. Alexander, and Matthew T. Woodhouse
Atmos. Chem. Phys., 22, 14603–14630, https://doi.org/10.5194/acp-22-14603-2022, https://doi.org/10.5194/acp-22-14603-2022, 2022
Short summary
Short summary
Climate models have difficulty simulating Southern Ocean clouds, impacting how much sunlight reaches the surface. We use machine learning to group different cloud types observed from satellites and simulated in a climate model. We find the model does a poor job of simulating the same cloud type as what the satellite shows and, even when it does, the cloud properties and amount of reflected sunlight are incorrect. We have a lot of work to do to model clouds correctly over the Southern Ocean.
Prabhakar Shrestha, Jana Mendrok, and Dominik Brunner
Atmos. Chem. Phys., 22, 14095–14117, https://doi.org/10.5194/acp-22-14095-2022, https://doi.org/10.5194/acp-22-14095-2022, 2022
Short summary
Short summary
The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol dynamics and a radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. This is demonstrated using a case study of a deep convective storm, which showed that the strong updraft in the convective core of the storm produced aerosol-tower-like features, which affected the size of the hydrometeors and the simulated polarimetric features (e.g., ZDR and KDP columns).
Jia He, Helene Brogniez, and Laurence Picon
Atmos. Chem. Phys., 22, 12591–12606, https://doi.org/10.5194/acp-22-12591-2022, https://doi.org/10.5194/acp-22-12591-2022, 2022
Short summary
Short summary
A 2003–2017 satellite-based atmospheric water vapour climate data record is used to assess climate models and reanalyses. The focus is on the tropical belt, whose regional variations in the hydrological cycle are related to the tropospheric overturning circulation. While there are similarities in the interannual variability, the major discrepancies can be explained by the presence of clouds, the representation of moisture fluxes at the surface and cloud processes in the models.
Silvia M. Calderón, Juha Tonttila, Angela Buchholz, Jorma Joutsensaari, Mika Komppula, Ari Leskinen, Liqing Hao, Dmitri Moisseev, Iida Pullinen, Petri Tiitta, Jian Xu, Annele Virtanen, Harri Kokkola, and Sami Romakkaniemi
Atmos. Chem. Phys., 22, 12417–12441, https://doi.org/10.5194/acp-22-12417-2022, https://doi.org/10.5194/acp-22-12417-2022, 2022
Short summary
Short summary
The spatial and temporal restrictions of observations and oversimplified aerosol representation in large eddy simulations (LES) limit our understanding of aerosol–stratocumulus interactions. In this closure study of in situ and remote sensing observations and outputs from UCLALES–SALSA, we have assessed the role of convective overturning and aerosol effects in two cloud events observed at the Puijo SMEAR IV station, Finland, a diurnal-high aerosol case and a nocturnal-low aerosol case.
Cited articles
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M.,
Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R.,
Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P.,
Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: A System for Large-Scale
Machine Learning, in: Proceedings of the 12th USENIX Conference on Operating
Systems Design and Implementation, OSDI'16, [code], USENIX Association,
USA, 265–283, 2016. a, b
Behnel, S., Bradshaw, R., Citro, C., Dalcin, L., Seljebotn, D. S., and Smith,
K.: Cython: The Best of Both Worlds, Comput. Sci. Eng., 13,
31–39, https://doi.org/10.1109/MCSE.2010.118, 2011. a
Bender, F. A.-M., Engström, A., Wood, R., and Charlson, R. J.: Evaluation of
Hemispheric Asymmetries in Marine Cloud Radiative Properties, J. Climate, 30, 4131–4147, https://doi.org/10.1175/JCLI-D-16-0263.1, 2017. a
Bjordal, J., Storelvmo, T., Alterskjær, K., and Carlsen, T.: Equilibrium
climate sensitivity above 5 ∘C plausible due to
state-dependent cloud feedback, Nat. Geosci., 13, 718–721,
https://doi.org/10.1038/s41561-020-00649-1, 2020. a
Bretherton, C. S. and Caldwell, P. M.: Combining Emergent Constraints for
Climate Sensitivity, J. Climate, 33, 7413–7430,
https://doi.org/10.1175/JCLI-D-19-0911.1, 2020. a
CERES: CERES Data Products, [data set],
https://ceres.larc.nasa.gov/data/, last access: 5 December
2022. a
Cesana, G., Del Genio, A. D., and Chepfer, H.: The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), Earth Syst. Sci. Data, 11, 1745–1764, https://doi.org/10.5194/essd-11-1745-2019, 2019. a
Cho, N., Tan, J., and Oreopoulos, L.: Classifying Planetary Cloudiness with an
Updated Set of MODIS Cloud Regimes, J. Appl. Meteorol. Clim., 60, 981–997, https://doi.org/10.1175/JAMC-D-20-0247.1, 2021. a
CMIP5: CMIP5 Data Search, [data set],
https://esgf-node.llnl.gov/search/cmip5/, last access: 5
December 2022. a
CMIP6: CMIP6 Data Search, [data set],
https://esgf-node.llnl.gov/search/cmip6/, last access: 5
December 2022. a
Doelling, D. R., Loeb, N. G., Keyes, D. F., Nordeen, M. L., Morstad, D.,
Nguyen, C., Wielicki, B. A., Young, D. F., and Sun, M.: Geostationary
Enhanced Temporal Interpolation for CERES Flux Products, J. Atmos. Ocean. Tech., 30, 1072–1090,
https://doi.org/10.1175/JTECH-D-12-00136.1, 2013. a
Dong, Y., Armour, K. C., Zelinka, M. D., Proistosescu, C., Battisti, D. S.,
Zhou, C., and Andrews, T.: Intermodel Spread in the Pattern Effect and Its
Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models, J. Climate, 33, 7755–7775, https://doi.org/10.1175/JCLI-D-19-1011.1, 2020. a
Drönner, J., Korfhage, N., Egli, S., Mühling, M., Thies, B., Bendix, J.,
Freisleben, B., and Seeger, B.: Fast Cloud Segmentation Using Convolutional
Neural Networks, Remote Sens., 10, 1782, https://doi.org/10.3390/rs10111782, 2018. a
Engström, A., Bender, F. A.-M., Charlson, R. J., and Wood, R.: The nonlinear
relationship between albedo and cloud fraction on near-global, monthly mean
scale in observations and in the CMIP5 model ensemble, Geophys. Res. Lett., 42, 9571–9578, https://doi.org/10.1002/2015GL066275, 2015. a
ERA5: ERA5, [data set],
https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5,
last access: 5 December 2022. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b
Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G.,
Caldwell, P., Collins, W. D., Gier, B. K., Hall, A. D., Hoffman, F. M.,
Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J. P.,
Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A. G.,
Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B. M., Santer, B. D.,
Sherwood, S. C., Simpson, I. R., Stouffer, R. J., and Williamson, M. S.:
Taking climate model evaluation to the next level, Nat. Clim. Change, 9,
102–110, https://doi.org/10.1038/s41558-018-0355-y, 2019. a
Flynn, C. M. and Mauritsen, T.: On the climate sensitivity and historical warming evolution in recent coupled model ensembles, Atmos. Chem. Phys., 20, 7829–7842, https://doi.org/10.5194/acp-20-7829-2020, 2020. a
FORCeS: The FORCeS Project: Constrained aerosol forcing for improved climate
projections, https://forces-project.eu, last access: 5
December 2022. a
Forster, P. M., Maycock, A. C., McKenna, C. M., and Smith, C. J.: Latest
climate models confirm need for urgent mitigation, Nat. Clim. Change, 10,
7–10, https://doi.org/10.1038/s41558-019-0660-0, 2020. a
Foster, M. J. and Heidinger, A.: PATMOS-x: Results from a Diurnally Corrected
30-yr Satellite Cloud Climatology, J. Climate, 26, 414–425,
https://doi.org/10.1175/JCLI-D-11-00666.1, 2013. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L.,
Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis
for Research and Applications, Version 2 (MERRA-2), J. Climate, 30,
5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a, b
Guo, Y., Cao, X., Liu, B., and Gao, M.: Cloud Detection for Satellite Imagery
Using Attention-Based U-Net Convolutional Neural Network, Symmetry, 12,
https://doi.org/10.3390/sym12061056, 2020. a
Haarsma, R., Acosta, M., Bakhshi, R., Bretonnière, P.-A., Caron, L.-P., Castrillo, M., Corti, S., Davini, P., Exarchou, E., Fabiano, F., Fladrich, U., Fuentes Franco, R., García-Serrano, J., von Hardenberg, J., Koenigk, T., Levine, X., Meccia, V. L., van Noije, T., van den Oord, G., Palmeiro, F. M., Rodrigo, M., Ruprich-Robert, Y., Le Sager, P., Tourigny, E., Wang, S., van Weele, M., and Wyser, K.: HighResMIP versions of EC-Earth: EC-Earth3P and EC-Earth3P-HR – description, model computational performance and basic validation, Geosci. Model Dev., 13, 3507–3527, https://doi.org/10.5194/gmd-13-3507-2020, 2020. a
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. a
Hahn, C. J., Rossow, W. B., and Warren, S. G.: ISCCP Cloud Properties
Associated with Standard Cloud Types Identified in Individual Surface
Observations, J. Climate, 14, 11–28,
https://doi.org/10.1175/1520-0442(2001)014<0011:ICPAWS>2.0.CO;2, 2001. a
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P.,
Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R.,
Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del
Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P.,
Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant,
T. E.: Array programming with NumPy, Nature, 585, 357–362,
https://doi.org/10.1038/s41586-020-2649-2, 2020. 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, b
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q.,
Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L.,
Watanabe, M., and Williamson, D.: The Art and Science of Climate Model
Tuning, B. Am. Meteorol. Soc., 98, 589–602,
https://doi.org/10.1175/BAMS-D-15-00135.1, 2017. a
Jakob, C. and Tselioudis, G.: Objective identification of cloud regimes in the
Tropical Western Pacific, Geophys. Res. Lett., 30,
https://doi.org/10.1029/2003GL018367, 2003. a
Jiménez-de-la-Cuesta, D. and Mauritsen, T.: Emergent constraints on
Earth's transient and equilibrium response to doubled CO2 from post-1970s
global warming, Nat. Geosci., 12, 902–905,
https://doi.org/10.1038/s41561-019-0463-y, 2019. a
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017. 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
Konsta, D., Dufresne, J.-L., Chepfer, H., Vial, J., Koshiro, T., Kawai, H.,
Bodas-Salcedo, A., Roehrig, R., Watanabe, M., and Ogura, T.: Low-Level Marine
Tropical Clouds in Six CMIP6 Models Are Too Few, Too Bright but Also Too
Compact and Too Homogeneous, Geophys. Res. Lett., 49,
e2021GL097593, https://doi.org/10.1029/2021GL097593,
2022. a
Kuma, P.: Code for the paper “Machine learning of cloud types in satellite
observations and climate models”, [code],
https://github.com/peterkuma/ml-clouds-2021/, last access: 5
December 2022. a
Kuma, P., McDonald, A. J., Morgenstern, O., Alexander, S. P., Cassano, J. J., Garrett, S., Halla, J., Hartery, S., Harvey, M. J., Parsons, S., Plank, G., Varma, V., and Williams, J.: Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations, Atmos. Chem. Phys., 20, 6607–6630, https://doi.org/10.5194/acp-20-6607-2020, 2020. a
Kuma, P., Bender, F. A.-M., Schuddeboom, A., McDonald, A. J., and Seland,
Ø.: Code accompanying the manuscript “Machine learning of cloud types
shows higher climate sensitivity is associated with lower cloud biases”, [code],
https://doi.org/10.5281/zenodo.7400793, 2022. a, b
Lenssen, N., Schmidt, G., Hansen, J., Menne, M., Persin, A., Ruedy, R., and
Zyss, D.: Improvements in the GISTEMP uncertainty model, J. Geophys. Res.-Atmos., 124, 6307–6326, https://doi.org/10.1029/2018JD029522, 2019. a
Liu, C., Yang, S., Di, D., Yang, Y., Zhou, C., Hu, X., and Sohn, B.-J.: A
Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral
Image, Adv. Atmos. Sci., 39, 1994–2007, https://doi.org/10.1007/s00376-021-0366-x,
2021. a
Liu, S. and Li, M.: Deep multimodal fusion for ground-based cloud
classification in weather station networks, EURASIP Journal on Wireless
Communications and Networking, 2018, https://doi.org/10.1186/s13638-018-1062-0, 2018. a
Loeb, N., Su, W., Doelling, D., Wong, T., Minnis, P., Thomas, S., and Miller,
W.: 5.03 – Earth’s Top-of-Atmosphere Radiation Budget, in: Comprehensive
Remote Sensing, edited by: Liang, S., Elsevier, Oxford,
67–84, https://doi.org/10.1016/B978-0-12-409548-9.10367-7, 2018. a
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Péan, C., Berger, S.,
Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy,
E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and
Zhou, B. (Eds.): Climate Change 2021: The Physical Science Basis, Contribution
of Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, Cambridge University Press, Cambridge, United
Kingdom, in press, 2021.
McDonald, A. J., Cassano, J. J., Jolly, B., Parsons, S., and Schuddeboom, A.:
An automated satellite cloud classification scheme using self-organizing
maps: Alternative ISCCP weather states, J. Geophys. Res.-Atmos., 121, 13009–13030, https://doi.org/10.1002/2016JD025199, 2016. a
McErlich, C., McDonald, A., Schuddeboom, A., and Silber, I.: Comparing
Satellite- and Ground-Based Observations of Cloud Occurrence Over High
Southern Latitudes, J. Geophys. Res.-Atmos., 126,
e2020JD033607, https://doi.org/10.1029/2020JD033607,
2021. a, b
Meehl, G. A., Senior, C. A., Eyring, V., Flato, G., Lamarque, J.-F., Stouffer,
R. J., Taylor, K. E., and Schlund, M.: Context for interpreting equilibrium
climate sensitivity and transient climate response from the CMIP6 Earth
system models, Sci. Adv., 6, eaba1981, https://doi.org/10.1126/sciadv.aba1981,
2020. a, b, c, d
MERRA-2: Modern-Era Retrospective analysis for Research and Applications,
Version 2, [data set], https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/,
last access: 5 December 2022. a
Met Office: Cartopy: a cartographic python library with a Matplotlib
interface, Exeter, Devon, [data set], https://scitools.org.uk/cartopy (last access: 16 December 2022),
2010. a
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and
Teller, E.: Equation of State Calculations by Fast Computing Machines,
J. Chem. Phys., 21, 1087–1092, https://doi.org/10.1063/1.1699114, 1953. a
Nam, C., Bony, S., Dufresne, J.-L., and Chepfer, H.: The “too few, too
bright” tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012gl053421, 2012. a
Nijsse, F. J. M. M., Cox, P. M., and Williamson, M. S.: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models, Earth Syst. Dynam., 11, 737–750, https://doi.org/10.5194/esd-11-737-2020, 2020. a, b
Olsson, B., Ynnerman, A., and Lenz, R.: Computing synthetic satellite images
from weather prediction data, in: Visualization and Data Analysis 2004,
edited by: Erbacher, R. F., Chen, P. C., Roberts, J. C., Gröhn, M. T., and
Börner, K., International Society for Optics and
Photonics, SPIE, 5295, 296–304, https://doi.org/10.1117/12.526829, 2004. a
Oreopoulos, L., Cho, N., Lee, D., and Kato, S.: Radiative effects of global
MODIS cloud regimes, J. Geophys. Res.-Atmos., 121,
2299–2317, https://doi.org/10.1002/2015JD024502, 2016. a, b
Renoult, M., Annan, J. D., Hargreaves, J. C., Sagoo, N., Flynn, C., Kapsch, M.-L., Li, Q., Lohmann, G., Mikolajewicz, U., Ohgaito, R., Shi, X., Zhang, Q., and Mauritsen, T.: A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP, Clim. Past, 16, 1715–1735, https://doi.org/10.5194/cp-16-1715-2020, 2020. a
Righi, M., Andela, B., Eyring, V., Lauer, A., Predoi, V., Schlund, M., Vegas-Regidor, J., Bock, L., Brötz, B., de Mora, L., Diblen, F., Dreyer, L., Drost, N., Earnshaw, P., Hassler, B., Koldunov, N., Little, B., Loosveldt Tomas, S., and Zimmermann, K.: Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview, Geosci. Model Dev., 13, 1179–1199, https://doi.org/10.5194/gmd-13-1179-2020, 2020. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for
Biomedical Image Segmentation, https://doi.org/10.48550/ARXIV.1505.04597, 2015. a, b
Rossow, W. B. and Schiffer, R. A.: Advances in Understanding Clouds from ISCCP,
B. Am. Meteorol. Soc., 80, 2261–2288,
https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999. a
Salvatier, J., Wiecki, T. V., and Fonnesbeck, C.: Probabilistic programming in
Python using PyMC3, PeerJ Comp. Sci., 2, e55,
https://doi.org/10.7717/peerj-cs.55, 2016. a, b
Schlund, M., Lauer, A., Gentine, P., Sherwood, S. C., and Eyring, V.: Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?, Earth Syst. Dynam., 11, 1233–1258, https://doi.org/10.5194/esd-11-1233-2020, 2020. a
Schmidt, G. A., Bader, D., Donner, L. J., Elsaesser, G. S., Golaz, J.-C., Hannay, C., Molod, A., Neale, R. B., and Saha, S.: Practice and philosophy of climate model tuning across six US modeling centers, Geosci. Model Dev., 10, 3207–3223, https://doi.org/10.5194/gmd-10-3207-2017, 2017. a
Schuddeboom, A., McDonald, A. J., Morgenstern, O., Harvey, M., and Parsons, S.:
Regional Regime-Based Evaluation of Present-Day General Circulation Model
Cloud Simulations Using Self-Organizing Maps, J. Geophys. Res.-Atmos., 123, 4259–4272, https://doi.org/10.1002/2017JD028196, 2018. a, b, c, d, e, f, g, h, i, j
Schuddeboom, A. J. and McDonald, A. J.: The Southern Ocean Radiative Bias,
Cloud Compensating Errors, and Equilibrium Climate Sensitivity in CMIP6
Models, J. Geophys. Res.-Atmos., 126, 1–16,
https://doi.org/10.1029/2021JD035310, 2021. a, b
Segal-Rozenhaimer, M., Li, A., Das, K., and Chirayath, V.: Cloud detection
algorithm for multi-modal satellite imagery using convolutional
neural-networks (CNN), Remote Sens. Environ., 237, 111446,
https://doi.org/10.1016/j.rse.2019.111446, 2020. a
Semmler, T., Jungclaus, J., Danek, C., Goessling, H. F., Koldunov, N. V.,
Rackow, T., and Sidorenko, D.: Ocean Model Formulation Influences Transient
Climate Response, J. Geophys. Res.-Oceans, 126,
e2021JC017633, https://doi.org/10.1029/2021JC017633,
2021. a
Shell, K. M., Kiehl, J. T., and Shields, C. A.: Using the Radiative Kernel
Technique to Calculate Climate Feedbacks in NCAR’s Community Atmospheric
Model, J. Climate, 21, 2269–2282, https://doi.org/10.1175/2007JCLI2044.1,
2008. a
Shendryk, Y., Rist, Y., Ticehurst, C., and Thorburn, P.: Deep learning for
multi-modal classification of cloud, shadow and land cover scenes in
PlanetScope and Sentinel-2 imagery, ISPRS J. Photogr. Remote Sens., 157, 124–136, https://doi.org/10.1016/j.isprsjprs.2019.08.018, 2019. a
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M.,
Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J.,
Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L.,
Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris,
J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and
Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple
Lines of Evidence, Rev. Geophys., 58, e2019RG000678,
https://doi.org/10.1029/2019RG000678, 2020. a, b, c, d
Shi, C., Wang, C., Wang, Y., and Xiao, B.: Deep Convolutional Activations-Based
Features for Ground-Based Cloud Classification, IEEE Geosci. Remote
Sens., 14, 816–820, https://doi.org/10.1109/lgrs.2017.2681658, 2017. a
Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., and Shields,
C. A.: Quantifying Climate Feedbacks Using Radiative Kernels, J. Climate, 21, 3504–3520, https://doi.org/10.1175/2007JCLI2110.1, 2008. a
Stengel, M., Stapelberg, S., Sus, O., Finkensieper, S., Würzler, B., Philipp, D., Hollmann, R., Poulsen, C., Christensen, M., and McGarragh, G.: Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties, Earth Syst. Sci. Data, 12, 41–60, https://doi.org/10.5194/essd-12-41-2020, 2020. a
Stocker, T., Qin, D., Plattner, G.-K., Tignor, M., Allen, S., Boschung, J.,
Nauels, A., Xia, Y., Bex, V., and Midgley, P., eds.: Climate Change 2013: The
Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA, https://doi.org/10.1017/CBO9781107415324, 2014.
Tange, O.: Gnu parallel-the command-line power tool, The USENIX
Magazine, 36, 42–47, 2011. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the
Experiment Design, B. Am. Meteorol. Soc., 93,
485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
The pandas development team: pandas-dev/pandas: Pandas,
https://doi.org/10.5281/zenodo.3509134, 2020. a
Tiedtke, M.: Representation of Clouds in Large-Scale Models, Mon. Weather Rev., 121, 3040–3061,
https://doi.org/10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2, 1993. a
Tokarska, K. B., Stolpe, M. B., Sippel, S., Fischer, E. M., Smith, C. J.,
Lehner, F., and Knutti, R.: Past warming trend constrains future warming in
CMIP6 models, Sci. Adv., 6, eaaz9549, https://doi.org/10.1126/sciadv.aaz9549,
2020. a
Unidata, U. C. f. A. R.: Historical Unidata Internet Data Distribution (IDD)
Global Observational Data, [data set], https://doi.org/10.5065/9235-WJ24, 2003. a, b, c
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N.,
Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat,
İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J.,
Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M.,
Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., Vijaykumar, A., Bardelli,
A. P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem,
A., Woods, C. N., Fulton, C., Masson, C., Häggström, C., Fitzgerald, C.,
Nicholson, D. A., Hagen, D. R., Pasechnik, D. V., Olivetti, E., Martin, E.,
Wieser, E., Silva, F., Lenders, F., Wilhelm, F., Young, G., Price, G. A.,
Ingold, G.-L., Allen, G. E., Lee, G. R., Audren, H., Probst, I., Dietrich,
J. P., Silterra, J., Webber, J. T., Slavič, J., Nothman, J., Buchner,
J., Kulick, J., Schönberger, J. L., de Miranda Cardoso, J. V., Reimer, J.,
Harrington, J., Rodríguez, J. L. C., Nunez-Iglesias, J., Kuczynski,
J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M., Bolingbroke, M.,
Tartre, M., Pak, M., Smith, N. J., Nowaczyk, N., Shebanov, N., Pavlyk, O.,
Brodtkorb, P. A., Lee, P., McGibbon, R. T., Feldbauer, R., Lewis, S., Tygier,
S., Sievert, S., Vigna, S., Peterson, S., More, S., Pudlik, T., Oshima, T.,
Pingel, T. J., Robitaille, T. P., Spura, T., Jones, T. R., Cera, T., Leslie,
T., Zito, T., Krauss, T., Upadhyay, U., Halchenko, Y. O., and and, Y. V.-B.:
SciPy 1.0: fundamental algorithms for scientific computing in Python,
Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a
Volodin, E.: The Mechanisms of Cloudiness Evolution Responsible for Equilibrium
Climate Sensitivity in Climate Model INM-CM4-8, Geophys. Res. Lett.,
48, e2021GL096204, https://doi.org/10.1029/2021GL096204,
2021. a
Wall, C. J., Hartmann, D. L., and Ma, P.-L.: Instantaneous linkages between
clouds and large-scale meteorology over the Southern Ocean in observations
and a climate model, J. Climate, 30, 9455–9474,
https://doi.org/10.1175/JCLI-D-17-0156.1, 2017. a
Wessel, P. and Smith, W. H. F.: A global, self-consistent, hierarchical,
high-resolution shoreline database, J. Geophys. Res.-Sol. Ea., 101, 8741–8743, https://doi.org/10.1029/96JB00104, 1996. a, b
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee, R. B., Smith, G. L.,
and Cooper, J. E.: Clouds and the Earth's Radiant Energy System (CERES): An
Earth Observing System Experiment, B. Am. Meteorol. Soc., 77, 853–868, https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2,
1996. a
Wilks, D. S.: Chapter 9 – Forecast Verification, in: Statistical Methods in
the Atmospheric Sciences (Fourth Edition), edited by: Wilks, D. S.,
Elsevier, 4 Edn., 369–483,
https://doi.org/10.1016/B978-0-12-815823-4.00009-2, 2019. a
WMO: Global Observing System,
https://public.wmo.int/en/programmes/global-observing-system(last access: 16 December 2022),
2021b. a
Wohlfarth, K., Schröer, C., Klaß, M., Hakenes, S., Venhaus, M., Kauffmann,
S., Wilhelm, T., and Wohler, C.: Dense Cloud Classification on Multispectral
Satellite Imagery, in: 2018 10th IAPR Workshop on Pattern Recognition in
Remote Sensing (PRRS), 1–6, https://doi.org/10.1109/PRRS.2018.8486379, 2018. a
Wyser, K., van Noije, T., Yang, S., von Hardenberg, J., O'Donnell, D., and Döscher, R.: On the increased climate sensitivity in the EC-Earth model from CMIP5 to CMIP6, Geosci. Model Dev., 13, 3465–3474, https://doi.org/10.5194/gmd-13-3465-2020, 2020. a
Ye, L., Cao, Z., and Xiao, Y.: DeepCloud: Ground-Based Cloud Image
Categorization Using Deep Convolutional Features, IEEE Transactions on
Geosci. Remote Sens., 55, 5729–5740,
https://doi.org/10.1109/TGRS.2017.2712809, 2017. a
Zelinka, M. D.: Tables of ECS, Effective Radiative Forcing, and Radiative
Feedbacks,
https://github.com/mzelinka/cmip56_forcing_feedback_ecs (last
access: 26 January 2022), 2021.
a
Zelinka, M. D., Klein, S. A., Qin, Y., and Myers, T. A.: Evaluating Climate
Models’ Cloud Feedbacks Against Expert Judgment, J. Geophys. Res.-Atmos., 127, e2021JD035198, https://doi.org/10.1029/2021JD035198,
2022. a, b
Zhang, J., Liu, P., Zhang, F., and Song, Q.: CloudNet: Ground-Based Cloud
Classification With Deep Convolutional Neural Network, Geophys. Res. Lett., 45, 8665–8672, https://doi.org/10.1029/2018GL077787, 2018. a
Zhao, M., Golaz, J.-C., Held, I. M., Ramaswamy, V., Lin, S.-J., Ming, Y.,
Ginoux, P., Wyman, B., Donner, L. J., Paynter, D., and Guo, H.: Uncertainty
in Model Climate Sensitivity Traced to Representations of Cumulus
Precipitation Microphysics, J. Climate, 29, 543–560,
https://doi.org/10.1175/JCLI-D-15-0191.1, 2016. a
Zhu, J., Poulsen, C. J., and Otto-Bliesner, B. L.: High climate sensitivity in
CMIP6 model not supported by paleoclimate, Nat. Clim. Change, 10,
378–379, https://doi.org/10.1038/s41558-020-0764-6, 2020. a
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Poulsen, C. J., Tierney, J. E.,
Lofverstrom, M., and DiNezio, P.: Assessment of Equilibrium Climate
Sensitivity of the Community Earth System Model Version 2 Through Simulation
of the Last Glacial Maximum, Geophys. Res. Lett., 48,
e2020GL091220, https://doi.org/10.1029/2020GL091220,
2021. a
Zhu, J., Otto-Bliesner, B. L., Brady, E. C., Gettelman, A., Bacmeister, J. T.,
Neale, R. B., Poulsen, C. J., Shaw, J. K., McGraw, Z. S., and Kay, J. E.: LGM
Paleoclimate Constraints Inform Cloud Parameterizations and Equilibrium
Climate Sensitivity in CESM2, J. Adv. Model. Earth Sy.,
14, e2021MS002776, https://doi.org/10.1029/2021MS002776,
2022. a
Short summary
We present a machine learning method for determining cloud types in climate model output and satellite observations based on ground observations of cloud genera. We analyse cloud type biases and changes with temperature in climate models and show that the bias is anticorrelated with climate sensitivity. Models simulating decreasing stratiform and increasing cumuliform clouds with increased CO2 concentration tend to have higher climate sensitivity than models simulating the opposite tendencies.
We present a machine learning method for determining cloud types in climate model output and...
Altmetrics
Final-revised paper
Preprint