Articles | Volume 16, issue 18
https://doi.org/10.5194/gmd-16-5427-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-5427-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Canadian Atmospheric Model version 5 (CanAM5.0.3)
Jason Neil Steven Cole
CORRESPONDING AUTHOR
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Knut von Salzen
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Jiangnan Li
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
John Scinocca
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
David Plummer
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Vivek Arora
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Norman McFarlane
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Michael Lazare
Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada
Murray MacKay
Environmental Numerical Weather Prediction Research Section, Environment and Climate Change Canada, Toronto, Ontario, Canada
Diana Verseghy
Climate Processes Section, Environment and Climate Change Canada, Toronto, Ontario, Canada
Related authors
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
Atmos. Meas. Tech., 18, 3095–3107, https://doi.org/10.5194/amt-18-3095-2025, https://doi.org/10.5194/amt-18-3095-2025, 2025
Short summary
Short summary
Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared, and the likelihood of them differing by 10 W m2 defines the closure.
Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason Cole, Gesa Meyer, Paul Bartlett, and Mickaël Lalande
EGUsphere, https://doi.org/10.5194/egusphere-2025-1264, https://doi.org/10.5194/egusphere-2025-1264, 2025
Short summary
Short summary
This study shows that an alternate snow cover fraction (SCF) parameterization significantly improves SCF simulated in the CLASSIC model in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS SCF observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and SCF.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
Short summary
Short summary
When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
Short summary
Short summary
The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Jason N. S. Cole, Howard W. Barker, Zhipeng Qu, Najda Villefranque, and Mark W. Shephard
Atmos. Meas. Tech., 16, 4271–4288, https://doi.org/10.5194/amt-16-4271-2023, https://doi.org/10.5194/amt-16-4271-2023, 2023
Short summary
Short summary
Measurements from the EarthCARE satellite mission will be used to retrieve profiles of cloud and aerosol properties. These retrievals are combined with auxiliary information about surface properties and atmospheric state, e.g., temperature and water vapor. This information allows computation of 1D and 3D solar and thermal radiative transfer for small domains, which are compared with coincident radiometer observations to continually assess EarthCARE retrievals.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
Short summary
Short summary
This paper describes EarthCARE’s L2 product ACM-3D. It includes the scene construction algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provides the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
Geosci. Model Dev., 18, 4399–4416, https://doi.org/10.5194/gmd-18-4399-2025, https://doi.org/10.5194/gmd-18-4399-2025, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone and greenhouse gases alone are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies and to underpin the next IPCC report.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
Atmos. Meas. Tech., 18, 3095–3107, https://doi.org/10.5194/amt-18-3095-2025, https://doi.org/10.5194/amt-18-3095-2025, 2025
Short summary
Short summary
Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared, and the likelihood of them differing by 10 W m2 defines the closure.
Simone Tilmes, Ewa M. Bednarz, Andrin Jörimann, Daniele Visioni, Douglas E. Kinnison, Gabriel Chiodo, and David Plummer
Atmos. Chem. Phys., 25, 6001–6023, https://doi.org/10.5194/acp-25-6001-2025, https://doi.org/10.5194/acp-25-6001-2025, 2025
Short summary
Short summary
In this paper, we describe the details of a new multi-model intercomparison experiment to assess the effects of Stratospheric Aerosol Intervention (SAI) on stratospheric chemistry and dynamics and, therefore, ozone. Second, we discuss the advantages and differences of the more constrained experiment compared to fully interactive model experiments. This way, we advance the process-level understanding of the drivers of SAI-induced atmospheric responses.
Patrick E. Sheese, Kaley A. Walker, Chris D. Boone, and David A. Plummer
Atmos. Chem. Phys., 25, 5199–5213, https://doi.org/10.5194/acp-25-5199-2025, https://doi.org/10.5194/acp-25-5199-2025, 2025
Short summary
Short summary
Observations from Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS) are used to examine global stratospheric water vapour trends for 2004–2021. The satellite measurements are used to quantify trend contributions arising from changes in tropical tropopause temperatures, general circulation patterns, and methane concentrations. While most of the observed trends can be explained by these changes, there remains an unaccounted-for and increasing source of water vapour in the lower mid-stratosphere at mid-latitudes, which is discussed.
Zhihong Zhuo, Xinyue Wang, Yunqian Zhu, Ewa M. Bednarz, Eric Fleming, Peter R. Colarco, Shingo Watanabe, David Plummer, Georgiy Stenchikov, William Randel, Adam Bourassa, Valentina Aquila, Takashi Sekiya, Mark R. Schoeberl, Simone Tilmes, Wandi Yu, Jun Zhang, Paul J. Kushner, and Francesco S. R. Pausata
EGUsphere, https://doi.org/10.5194/egusphere-2025-1505, https://doi.org/10.5194/egusphere-2025-1505, 2025
Short summary
Short summary
The 2022 Hunga eruption caused unprecedented stratospheric water injection, triggering unique atmospheric impacts. This study combines observations and model simulations, projecting a stratospheric water vapor anomaly lasting 4–7 years, with significant temperature variations and ozone depletion in the upper atmosphere lasting 7–10 years. These findings offer critical insights into the role of stratospheric water vapor in shaping climate and atmospheric chemistry.
Laura N. Saunders, Kaley A. Walker, Gabriele P. Stiller, Thomas von Clarmann, Florian Haenel, Hella Garny, Harald Bönisch, Chris D. Boone, Ariana E. Castillo, Andreas Engel, Johannes C. Laube, Marianna Linz, Felix Ploeger, David A. Plummer, Eric A. Ray, and Patrick E. Sheese
Atmos. Chem. Phys., 25, 4185–4209, https://doi.org/10.5194/acp-25-4185-2025, https://doi.org/10.5194/acp-25-4185-2025, 2025
Short summary
Short summary
We present a 17-year stratospheric age-of-air dataset derived from ACE-FTS satellite measurements of sulfur hexafluoride. This is the longest continuous, global, and vertically resolved age of air time series available to date. In this paper, we show that this dataset agrees well with age-of-air datasets based on measurements from other instruments. We also present trends in the midlatitude lower stratosphere that indicate changes in the global circulation that are predicted by climate models.
Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason Cole, Gesa Meyer, Paul Bartlett, and Mickaël Lalande
EGUsphere, https://doi.org/10.5194/egusphere-2025-1264, https://doi.org/10.5194/egusphere-2025-1264, 2025
Short summary
Short summary
This study shows that an alternate snow cover fraction (SCF) parameterization significantly improves SCF simulated in the CLASSIC model in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS SCF observations over the Northern Hemisphere. We also link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and SCF.
Manon Maisonnier, Maoyuan Feng, David Bastviken, Sandra Arndt, Ronny Lauerwald, Aidin Jabbari, Goulven Gildas Laruelle, Murray D. MacKay, Zeli Tan, Wim Thiery, and Pierre Regnier
EGUsphere, https://doi.org/10.5194/egusphere-2025-1306, https://doi.org/10.5194/egusphere-2025-1306, 2025
Short summary
Short summary
A new process-based modelling framework, FLaMe v1.0 (Fluxes of Lake Methane version 1.0), is developed to simulate methane (CH4) emissions from lakes at large scales. FLaMe couples the dynamics of organic carbon, oxygen and methane in lakes and rests on an innovative, computationally efficient lake clustering approach for the simulation of CH4 emissions across a large number of lakes. The model evaluation suggests that FLaMe captures the sub-annual and spatial variability of CH4 emissions well.
Tomohiro Hajima, Michio Kawamiya, Akihiko Ito, Kaoru Tachiiri, Chris D. Jones, Vivek Arora, Victor Brovkin, Roland Séférian, Spencer Liddicoat, Pierre Friedlingstein, and Elena Shevliakova
Biogeosciences, 22, 1447–1473, https://doi.org/10.5194/bg-22-1447-2025, https://doi.org/10.5194/bg-22-1447-2025, 2025
Short summary
Short summary
This study analyzes atmospheric CO2 concentrations and global carbon budgets simulated by multiple Earth system models, using several types of simulations (CO2 concentration- and emission-driven experiments). We successfully identified problems with regard to the global carbon budget in each model. We also found urgent issues with regard to land use change CO2 emissions that should be solved in the latest generation of models.
Nikolina Mileva, Julia Pongratz, Vivek K. Arora, Akihiko Ito, Sebastiaan Luyssaert, Sonali S. McDermid, Paul A. Miller, Daniele Peano, Roland Séférian, Yanwu Zhang, and Wolfgang Buermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-979, https://doi.org/10.5194/egusphere-2025-979, 2025
Short summary
Short summary
Despite forests being so important for mitigating climate change, there are still uncertainties about how much the changes in forest cover contribute to the cooling/warming of the climate. Climate models and real-world observations often disagree about the magnitude and even the direction of these changes. We constrain climate models scenarios of widespread deforestation with satellite and in-situ data and show that models still have difficulties representing the movement of heat and water.
Ruth A. R. Digby, Knut von Salzen, Adam H. Monahan, Nathan P. Gillett, and Jiangnan Li
Atmos. Chem. Phys., 25, 3109–3130, https://doi.org/10.5194/acp-25-3109-2025, https://doi.org/10.5194/acp-25-3109-2025, 2025
Short summary
Short summary
The refractive index of black carbon (BCRI), which determines how much energy black carbon absorbs and scatters, is difficult to measure, and different climate models use different values. We show that varying the BCRI across commonly used values can increase absorbing aerosol optical depth by 42 % and the warming effect from interactions between black carbon and radiation by 47 %, an appreciable fraction of the overall spread between models reported in recent literature assessments.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha
Hydrol. Earth Syst. Sci., 29, 291–312, https://doi.org/10.5194/hess-29-291-2025, https://doi.org/10.5194/hess-29-291-2025, 2025
Short summary
Short summary
This study presents a Canada-wide assessment of climate change impacts on the hydro-climatology of the region's major river basins. We find that precipitation, runoff, and temperature are all expected to increase over Canada in the future. The northerly Mackenzie and Yukon rivers are relatively less affected by climate change compared to the southerly Fraser and Columbia rivers, which are located in the milder northwestern Pacific region.
Yunqian Zhu, Hideharu Akiyoshi, Valentina Aquila, Elisabeth Asher, Ewa M. Bednarz, Slimane Bekki, Christoph Brühl, Amy H. Butler, Parker Case, Simon Chabrillat, Gabriel Chiodo, Margot Clyne, Lola Falletti, Peter R. Colarco, Eric Fleming, Andrin Jörimann, Mahesh Kovilakam, Gerbrand Koren, Ales Kuchar, Nicolas Lebas, Qing Liang, Cheng-Cheng Liu, Graham Mann, Michael Manyin, Marion Marchand, Olaf Morgenstern, Paul Newman, Luke D. Oman, Freja F. Østerstrøm, Yifeng Peng, David Plummer, Ilaria Quaglia, William Randel, Samuel Rémy, Takashi Sekiya, Stephen Steenrod, Timofei Sukhodolov, Simone Tilmes, Kostas Tsigaridis, Rei Ueyama, Daniele Visioni, Xinyue Wang, Shingo Watanabe, Yousuke Yamashita, Pengfei Yu, Wandi Yu, Jun Zhang, and Zhihong Zhuo
EGUsphere, https://doi.org/10.5194/egusphere-2024-3412, https://doi.org/10.5194/egusphere-2024-3412, 2024
Short summary
Short summary
To understand the climate impact of the 2022 Hunga volcanic eruption, we developed a climate model-observation comparison project. The paper describes the protocols and models that participate in the experiments. We designed several experiments to achieve our goal of this activity: 1. evaluate the climate model performance; 2. understand the Earth system responses to this eruption.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
Short summary
Short summary
This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Misa Ishizawa, Douglas Chan, Doug Worthy, Elton Chan, Felix Vogel, Joe R. Melton, and Vivek K. Arora
Atmos. Chem. Phys., 24, 10013–10038, https://doi.org/10.5194/acp-24-10013-2024, https://doi.org/10.5194/acp-24-10013-2024, 2024
Short summary
Short summary
Methane (CH4) emissions in Canada for 2007–2017 were estimated using Canada’s surface greenhouse gas measurements. The estimated emissions show no significant trend, but emission uncertainty was reduced as more measurement sites became available. Notably for climate change, we find the wetland CH4 emissions show a positive correlation with surface air temperature in summer. Canada’s measurement network could monitor future CH4 emission changes and compliance with climate change mitigation goals.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, and Libo Wang
Biogeosciences, 21, 3339–3371, https://doi.org/10.5194/bg-21-3339-2024, https://doi.org/10.5194/bg-21-3339-2024, 2024
Short summary
Short summary
Terrestrial biosphere models can either prescribe the geographical distribution of biomes or simulate them dynamically, capturing climate-change-driven biome shifts. We isolate and examine the differences between these different land cover implementations. We find that the simulated terrestrial carbon sink at the end of the 21st century is twice as large in simulations with dynamic land cover than in simulations with prescribed land cover due to important range shifts in the Arctic and Amazon.
Felicia Kolonjari, Patrick E. Sheese, Kaley A. Walker, Chris D. Boone, David A. Plummer, Andreas Engel, Stephen A. Montzka, David E. Oram, Tanja Schuck, Gabriele P. Stiller, and Geoffrey C. Toon
Atmos. Meas. Tech., 17, 2429–2449, https://doi.org/10.5194/amt-17-2429-2024, https://doi.org/10.5194/amt-17-2429-2024, 2024
Short summary
Short summary
The Canadian Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) satellite instrument is currently providing the only vertically resolved chlorodifluoromethane (HCFC-22) measurements from space. This study assesses the most current ACE-FTS HCFC-22 data product in the upper troposphere and lower stratosphere, as well as modelled HCFC-22 from a 39-year run of the Canadian Middle Atmosphere Model (CMAM39) in the same region.
Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang
Atmos. Chem. Phys., 24, 2077–2097, https://doi.org/10.5194/acp-24-2077-2024, https://doi.org/10.5194/acp-24-2077-2024, 2024
Short summary
Short summary
The COVID-19 lockdowns reduced aerosol emissions. We ask whether these reductions affected regional aerosol optical depth (AOD) and compare the observed changes to predictions from Earth system models. Only India has an observed AOD reduction outside of typical variability. Models overestimate the response in some regions, but when key biases have been addressed, the agreement is improved. Our results suggest that current models can realistically predict the effects of future emission changes.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
Short summary
Short summary
When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
Victoria A. Flood, Kimberly Strong, Cynthia H. Whaley, Kaley A. Walker, Thomas Blumenstock, James W. Hannigan, Johan Mellqvist, Justus Notholt, Mathias Palm, Amelie N. Röhling, Stephen Arnold, Stephen Beagley, Rong-You Chien, Jesper Christensen, Makoto Deushi, Srdjan Dobricic, Xinyi Dong, Joshua S. Fu, Michael Gauss, Wanmin Gong, Joakim Langner, Kathy S. Law, Louis Marelle, Tatsuo Onishi, Naga Oshima, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Manu A. Thomas, Svetlana Tsyro, and Steven Turnock
Atmos. Chem. Phys., 24, 1079–1118, https://doi.org/10.5194/acp-24-1079-2024, https://doi.org/10.5194/acp-24-1079-2024, 2024
Short summary
Short summary
It is important to understand the composition of the Arctic atmosphere and how it is changing. Atmospheric models provide simulations that can inform policy. This study examines simulations of CH4, CO, and O3 by 11 models. Model performance is assessed by comparing results matched in space and time to measurements from five high-latitude ground-based infrared spectrometers. This work finds that models generally underpredict the concentrations of these gases in the Arctic troposphere.
Ali Asaadi, Jörg Schwinger, Hanna Lee, Jerry Tjiputra, Vivek Arora, Roland Séférian, Spencer Liddicoat, Tomohiro Hajima, Yeray Santana-Falcón, and Chris D. Jones
Biogeosciences, 21, 411–435, https://doi.org/10.5194/bg-21-411-2024, https://doi.org/10.5194/bg-21-411-2024, 2024
Short summary
Short summary
Carbon cycle feedback metrics are employed to assess phases of positive and negative CO2 emissions. When emissions become negative, we find that the model disagreement in feedback metrics increases more strongly than expected from the assumption that the uncertainties accumulate linearly with time. The geographical patterns of such metrics over land highlight that differences in response between tropical/subtropical and temperate/boreal ecosystems are a major source of model disagreement.
Michael Sigmond, James Anstey, Vivek Arora, Ruth Digby, Nathan Gillett, Viatcheslav Kharin, William Merryfield, Catherine Reader, John Scinocca, Neil Swart, John Virgin, Carsten Abraham, Jason Cole, Nicolas Lambert, Woo-Sung Lee, Yongxiao Liang, Elizaveta Malinina, Landon Rieger, Knut von Salzen, Christian Seiler, Clint Seinen, Andrew Shao, Reinel Sospedra-Alfonso, Libo Wang, and Duo Yang
Geosci. Model Dev., 16, 6553–6591, https://doi.org/10.5194/gmd-16-6553-2023, https://doi.org/10.5194/gmd-16-6553-2023, 2023
Short summary
Short summary
We present a new activity which aims to organize the analysis of biases in the Canadian Earth System model (CanESM) in a systematic manner. Results of this “Analysis for Development” (A4D) activity includes a new CanESM version, CanESM5.1, which features substantial improvements regarding the simulation of dust and stratospheric temperatures, a second CanESM5.1 variant with reduced climate sensitivity, and insights into potential avenues to reduce various other model biases.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
Short summary
Short summary
The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Jason N. S. Cole, Howard W. Barker, Zhipeng Qu, Najda Villefranque, and Mark W. Shephard
Atmos. Meas. Tech., 16, 4271–4288, https://doi.org/10.5194/amt-16-4271-2023, https://doi.org/10.5194/amt-16-4271-2023, 2023
Short summary
Short summary
Measurements from the EarthCARE satellite mission will be used to retrieve profiles of cloud and aerosol properties. These retrievals are combined with auxiliary information about surface properties and atmospheric state, e.g., temperature and water vapor. This information allows computation of 1D and 3D solar and thermal radiative transfer for small domains, which are compared with coincident radiometer observations to continually assess EarthCARE retrievals.
Marina Friedel, Gabriel Chiodo, Timofei Sukhodolov, James Keeble, Thomas Peter, Svenja Seeber, Andrea Stenke, Hideharu Akiyoshi, Eugene Rozanov, David Plummer, Patrick Jöckel, Guang Zeng, Olaf Morgenstern, and Béatrice Josse
Atmos. Chem. Phys., 23, 10235–10254, https://doi.org/10.5194/acp-23-10235-2023, https://doi.org/10.5194/acp-23-10235-2023, 2023
Short summary
Short summary
Previously, it has been suggested that springtime Arctic ozone depletion might worsen in the coming decades due to climate change, which might counteract the effect of reduced ozone-depleting substances. Here, we show with different chemistry–climate models that springtime Arctic ozone depletion will likely decrease in the future. Further, we explain why models show a large spread in the projected development of Arctic ozone depletion and use the model spread to constrain future projections.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle
Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023, https://doi.org/10.5194/esd-14-767-2023, 2023
Short summary
Short summary
Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that variability in N processes across models is large. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain, and more are necessary to guide the development of N cycling in models.
Libo Wang, Vivek K. Arora, Paul Bartlett, Ed Chan, and Salvatore R. Curasi
Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, https://doi.org/10.5194/bg-20-2265-2023, 2023
Short summary
Short summary
Plant functional types (PFTs) are groups of plant species used to represent vegetation distribution in land surface models. There are large uncertainties associated with existing methods for mapping land cover datasets to PFTs. This study demonstrates how fine-resolution tree cover fraction and land cover datasets can be used to inform the PFT mapping process and reduce the uncertainties. The proposed largely objective method makes it easier to implement new land cover products in models.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
Short summary
Short summary
This paper describes EarthCARE’s L2 product ACM-3D. It includes the scene construction algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provides the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Vivek K. Arora, Christian Seiler, Libo Wang, and Sian Kou-Giesbrecht
Biogeosciences, 20, 1313–1355, https://doi.org/10.5194/bg-20-1313-2023, https://doi.org/10.5194/bg-20-1313-2023, 2023
Short summary
Short summary
The behaviour of natural systems is now very often represented through mathematical models. These models represent our understanding of how nature works. Of course, nature does not care about our understanding. Since our understanding is not perfect, evaluating models is challenging, and there are uncertainties. This paper illustrates this uncertainty for land models and argues that evaluating models in light of the uncertainty in various components provides useful information.
Cynthia H. Whaley, Kathy S. Law, Jens Liengaard Hjorth, Henrik Skov, Stephen R. Arnold, Joakim Langner, Jakob Boyd Pernov, Garance Bergeron, Ilann Bourgeois, Jesper H. Christensen, Rong-You Chien, Makoto Deushi, Xinyi Dong, Peter Effertz, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Greg Huey, Ulas Im, Rigel Kivi, Louis Marelle, Tatsuo Onishi, Naga Oshima, Irina Petropavlovskikh, Jeff Peischl, David A. Plummer, Luca Pozzoli, Jean-Christophe Raut, Tom Ryerson, Ragnhild Skeie, Sverre Solberg, Manu A. Thomas, Chelsea Thompson, Kostas Tsigaridis, Svetlana Tsyro, Steven T. Turnock, Knut von Salzen, and David W. Tarasick
Atmos. Chem. Phys., 23, 637–661, https://doi.org/10.5194/acp-23-637-2023, https://doi.org/10.5194/acp-23-637-2023, 2023
Short summary
Short summary
This study summarizes recent research on ozone in the Arctic, a sensitive and rapidly warming region. We find that the seasonal cycles of near-surface atmospheric ozone are variable depending on whether they are near the coast, inland, or at high altitude. Several global model simulations were evaluated, and we found that because models lack some of the ozone chemistry that is important for the coastal Arctic locations, they do not accurately simulate ozone there.
Paul S. Jeffery, Kaley A. Walker, Chris E. Sioris, Chris D. Boone, Doug Degenstein, Gloria L. Manney, C. Thomas McElroy, Luis Millán, David A. Plummer, Niall J. Ryan, Patrick E. Sheese, and Jiansheng Zou
Atmos. Chem. Phys., 22, 14709–14734, https://doi.org/10.5194/acp-22-14709-2022, https://doi.org/10.5194/acp-22-14709-2022, 2022
Short summary
Short summary
The upper troposphere–lower stratosphere is one of the most variable regions in the atmosphere. To improve our understanding of water vapour and ozone concentrations in this region, climatologies have been developed from 14 years of measurements from three Canadian satellite instruments. Horizontal and vertical coordinates have been chosen to minimize the effects of variability. To aid in analysis, model simulations have been used to characterize differences between instrument climatologies.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Charles D. Koven, Vivek K. Arora, Patricia Cadule, Rosie A. Fisher, Chris D. Jones, David M. Lawrence, Jared Lewis, Keith Lindsay, Sabine Mathesius, Malte Meinshausen, Michael Mills, Zebedee Nicholls, Benjamin M. Sanderson, Roland Séférian, Neil C. Swart, William R. Wieder, and Kirsten Zickfeld
Earth Syst. Dynam., 13, 885–909, https://doi.org/10.5194/esd-13-885-2022, https://doi.org/10.5194/esd-13-885-2022, 2022
Short summary
Short summary
We explore the long-term dynamics of Earth's climate and carbon cycles under a pair of contrasting scenarios to the year 2300 using six models that include both climate and carbon cycle dynamics. One scenario assumes very high emissions, while the second assumes a peak in emissions, followed by rapid declines to net negative emissions. We show that the models generally agree that warming is roughly proportional to carbon emissions but that many other aspects of the model projections differ.
Cynthia H. Whaley, Rashed Mahmood, Knut von Salzen, Barbara Winter, Sabine Eckhardt, Stephen Arnold, Stephen Beagley, Silvia Becagli, Rong-You Chien, Jesper Christensen, Sujay Manish Damani, Xinyi Dong, Konstantinos Eleftheriadis, Nikolaos Evangeliou, Gregory Faluvegi, Mark Flanner, Joshua S. Fu, Michael Gauss, Fabio Giardi, Wanmin Gong, Jens Liengaard Hjorth, Lin Huang, Ulas Im, Yugo Kanaya, Srinath Krishnan, Zbigniew Klimont, Thomas Kühn, Joakim Langner, Kathy S. Law, Louis Marelle, Andreas Massling, Dirk Olivié, Tatsuo Onishi, Naga Oshima, Yiran Peng, David A. Plummer, Olga Popovicheva, Luca Pozzoli, Jean-Christophe Raut, Maria Sand, Laura N. Saunders, Julia Schmale, Sangeeta Sharma, Ragnhild Bieltvedt Skeie, Henrik Skov, Fumikazu Taketani, Manu A. Thomas, Rita Traversi, Kostas Tsigaridis, Svetlana Tsyro, Steven Turnock, Vito Vitale, Kaley A. Walker, Minqi Wang, Duncan Watson-Parris, and Tahya Weiss-Gibbons
Atmos. Chem. Phys., 22, 5775–5828, https://doi.org/10.5194/acp-22-5775-2022, https://doi.org/10.5194/acp-22-5775-2022, 2022
Short summary
Short summary
Air pollutants, like ozone and soot, play a role in both global warming and air quality. Atmospheric models are often used to provide information to policy makers about current and future conditions under different emissions scenarios. In order to have confidence in those simulations, in this study we compare simulated air pollution from 18 state-of-the-art atmospheric models to measured air pollution in order to assess how well the models perform.
Hengqi Wang, Yiran Peng, Knut von Salzen, Yan Yang, Wei Zhou, and Delong Zhao
Geosci. Model Dev., 15, 2949–2971, https://doi.org/10.5194/gmd-15-2949-2022, https://doi.org/10.5194/gmd-15-2949-2022, 2022
Short summary
Short summary
The aerosol activation scheme is an important part of the general circulation model, but evaluations using observed data are mostly regional. This research introduced a numerically efficient aerosol activation scheme and evaluated it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The decent performance indicates that the scheme is suitable for simulations of cloud droplet number concentrations over wide conditions.
Julia Schmale, Sangeeta Sharma, Stefano Decesari, Jakob Pernov, Andreas Massling, Hans-Christen Hansson, Knut von Salzen, Henrik Skov, Elisabeth Andrews, Patricia K. Quinn, Lucia M. Upchurch, Konstantinos Eleftheriadis, Rita Traversi, Stefania Gilardoni, Mauro Mazzola, James Laing, and Philip Hopke
Atmos. Chem. Phys., 22, 3067–3096, https://doi.org/10.5194/acp-22-3067-2022, https://doi.org/10.5194/acp-22-3067-2022, 2022
Short summary
Short summary
Long-term data sets of Arctic aerosol properties from 10 stations across the Arctic provide evidence that anthropogenic influence on the Arctic atmospheric chemical composition has declined in winter, a season which is typically dominated by mid-latitude emissions. The number of significant trends in summer is smaller than in winter, and overall the pattern is ambiguous with some significant positive and negative trends. This reflects the mixed influence of natural and anthropogenic emissions.
Adam A. Scaife, Mark P. Baldwin, Amy H. Butler, Andrew J. Charlton-Perez, Daniela I. V. Domeisen, Chaim I. Garfinkel, Steven C. Hardiman, Peter Haynes, Alexey Yu Karpechko, Eun-Pa Lim, Shunsuke Noguchi, Judith Perlwitz, Lorenzo Polvani, Jadwiga H. Richter, John Scinocca, Michael Sigmond, Theodore G. Shepherd, Seok-Woo Son, and David W. J. Thompson
Atmos. Chem. Phys., 22, 2601–2623, https://doi.org/10.5194/acp-22-2601-2022, https://doi.org/10.5194/acp-22-2601-2022, 2022
Short summary
Short summary
Great progress has been made in computer modelling and simulation of the whole climate system, including the stratosphere. Since the late 20th century we also gained a much clearer understanding of how the stratosphere interacts with the lower atmosphere. The latest generation of numerical prediction systems now explicitly represents the stratosphere and its interaction with surface climate, and here we review its role in long-range predictions and projections from weeks to decades ahead.
Alexander J. Winkler, Ranga B. Myneni, Alexis Hannart, Stephen Sitch, Vanessa Haverd, Danica Lombardozzi, Vivek K. Arora, Julia Pongratz, Julia E. M. S. Nabel, Daniel S. Goll, Etsushi Kato, Hanqin Tian, Almut Arneth, Pierre Friedlingstein, Atul K. Jain, Sönke Zaehle, and Victor Brovkin
Biogeosciences, 18, 4985–5010, https://doi.org/10.5194/bg-18-4985-2021, https://doi.org/10.5194/bg-18-4985-2021, 2021
Short summary
Short summary
Satellite observations since the early 1980s show that Earth's greening trend is slowing down and that browning clusters have been emerging, especially in the last 2 decades. A collection of model simulations in conjunction with causal theory points at climatic changes as a key driver of vegetation changes in natural ecosystems. Most models underestimate the observed vegetation browning, especially in tropical rainforests, which could be due to an excessive CO2 fertilization effect in models.
Christian Seiler, Joe R. Melton, Vivek K. Arora, and Libo Wang
Geosci. Model Dev., 14, 2371–2417, https://doi.org/10.5194/gmd-14-2371-2021, https://doi.org/10.5194/gmd-14-2371-2021, 2021
Short summary
Short summary
This study evaluates how well the CLASSIC land surface model reproduces the energy, water, and carbon cycle when compared against a wide range of global observations. Special attention is paid to how uncertainties in the data used to drive and evaluate the model affect model skill. Our results show the importance of incorporating uncertainties when evaluating land surface models and that failing to do so may potentially misguide future model development.
Patrick E. Sheese, Kaley A. Walker, Chris D. Boone, Doug A. Degenstein, Felicia Kolonjari, David Plummer, Douglas E. Kinnison, Patrick Jöckel, and Thomas von Clarmann
Atmos. Meas. Tech., 14, 1425–1438, https://doi.org/10.5194/amt-14-1425-2021, https://doi.org/10.5194/amt-14-1425-2021, 2021
Short summary
Short summary
Output from climate chemistry models (CMAM, EMAC, and WACCM) is used to estimate the expected geophysical variability of ozone concentrations between coincident satellite instrument measurement times and geolocations. We use the Canadian ACE-FTS and OSIRIS instruments as a case study. Ensemble mean estimates are used to optimize coincidence criteria between the two instruments, allowing for the use of more coincident profiles while providing an estimate of the geophysical variation.
Ali Asaadi and Vivek K. Arora
Biogeosciences, 18, 669–706, https://doi.org/10.5194/bg-18-669-2021, https://doi.org/10.5194/bg-18-669-2021, 2021
Short summary
Short summary
More than a quarter of the current anthropogenic CO2 emissions are taken up by land, reducing the atmospheric CO2 growth rate. This is because of the CO2 fertilization effect which benefits 80 % of global vegetation. However, if nitrogen and phosphorus nutrients cannot keep up with increasing atmospheric CO2, the magnitude of this terrestrial ecosystem service may reduce in future. This paper implements nitrogen constraints on photosynthesis in a model to understand the mechanisms involved.
Arseniy Karagodin-Doyennel, Eugene Rozanov, Ales Kuchar, William Ball, Pavle Arsenovic, Ellis Remsberg, Patrick Jöckel, Markus Kunze, David A. Plummer, Andrea Stenke, Daniel Marsh, Doug Kinnison, and Thomas Peter
Atmos. Chem. Phys., 21, 201–216, https://doi.org/10.5194/acp-21-201-2021, https://doi.org/10.5194/acp-21-201-2021, 2021
Short summary
Short summary
The solar signal in the mesospheric H2O and CO was extracted from the CCMI-1 model simulations and satellite observations using multiple linear regression (MLR) analysis. MLR analysis shows a pronounced and statistically robust solar signal in both H2O and CO. The model results show a general agreement with observations reproducing a negative/positive solar signal in H2O/CO. The pattern of the solar signal varies among the considered models, reflecting some differences in the model setup.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
Short summary
Short summary
The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Lena R. Boysen, Victor Brovkin, Julia Pongratz, David M. Lawrence, Peter Lawrence, Nicolas Vuichard, Philippe Peylin, Spencer Liddicoat, Tomohiro Hajima, Yanwu Zhang, Matthias Rocher, Christine Delire, Roland Séférian, Vivek K. Arora, Lars Nieradzik, Peter Anthoni, Wim Thiery, Marysa M. Laguë, Deborah Lawrence, and Min-Hui Lo
Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, https://doi.org/10.5194/bg-17-5615-2020, 2020
Short summary
Short summary
We find a biogeophysically induced global cooling with strong carbon losses in a 20 million square kilometre idealized deforestation experiment performed by nine CMIP6 Earth system models. It takes many decades for the temperature signal to emerge, with non-local effects playing an important role. Despite a consistent experimental setup, models diverge substantially in their climate responses. This study offers unprecedented insights for understanding land use change effects in CMIP6 models.
Matt Amos, Paul J. Young, J. Scott Hosking, Jean-François Lamarque, N. Luke Abraham, Hideharu Akiyoshi, Alexander T. Archibald, Slimane Bekki, Makoto Deushi, Patrick Jöckel, Douglas Kinnison, Ole Kirner, Markus Kunze, Marion Marchand, David A. Plummer, David Saint-Martin, Kengo Sudo, Simone Tilmes, and Yousuke Yamashita
Atmos. Chem. Phys., 20, 9961–9977, https://doi.org/10.5194/acp-20-9961-2020, https://doi.org/10.5194/acp-20-9961-2020, 2020
Short summary
Short summary
We present an updated projection of Antarctic ozone hole recovery using an ensemble of chemistry–climate models. To do so, we employ a method, more advanced and skilful than the current multi-model mean standard, which is applicable to other ensemble analyses. It calculates the performance and similarity of the models, which we then use to weight the model. Calculating model similarity allows us to account for models which are constructed from similar components.
Cited articles
Anderson, E. A.: A point energy and mass balance model of a snow cover, Tech.
rep., United States National Weather Service,
https://repository.library.noaa.gov/view/noaa/6392, 1976. a
Arora, V. K. and Boer, G. J.: A parameterization of leaf phenology for the
terrestrial ecosystem component of climate models, Global Change Biol., 11,
39–59, https://doi.org/10.1111/j.1365-2486.2004.00890.x,
2005. a
Arora, V. K., Scinocca, J. F., Boer, G. J., Christian, J. R., Denman, K. L.,
Flato, G. M., Kharin, V. V., Lee, W. G., and Merryfield, W. J.: Carbon
emission limits required to satisfy future representative concentration
pathways of greenhouse gases, Geophys. Res. Lett., 38, L05805,
https://doi.org/10.1029/2010GL046270, 2011. a
Arora, V. K., Melton, J. R., and Plummer, D.: An assessment of natural methane fluxes simulated by the CLASS-CTEM model, Biogeosciences, 15, 4683–4709, https://doi.org/10.5194/bg-15-4683-2018, 2018. a
Bartlett, P. A. and Verseghy, D. L.: Modified treatment of intercepted snow
improves the simulated forest albedo in the Canadian Land Surface
Scheme, Hydrol. Process., 29, 3208–3226,
https://doi.org/10.1002/hyp.10431, 2015. a
Bartlett, P. A., MacKay, M. D., and Verseghy, D. L.: Modified snow algorithms
in the Canadian land surface scheme: Model runs and sensitivity analysis
at three boreal forest stands, Atmosphere-Ocean, 44, 207–222,
https://doi.org/10.3137/ao.440301, 2006. a
Baum, B. A., Yang, P., Heymsfield, A. J., Schmitt, C. G., Xie, Y., Bansemer,
A., Hu, Y.-X., and Zhang, Z.: Improvements in Shortwave Bulk Scattering and
Absorption Models for the Remote Sensing of Ice Clouds, J. Appl.
Meteorol. Climatol., 50, 1037–1056, https://doi.org/10.1175/2010JAMC2608.1,
2011. a
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein,
S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.:
COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043,
https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Cesana, G. and Chepfer, H.: Evaluation of the cloud thermodynamic phase in a
climate model using CALIPSO-GOCCP, J. Geophys. Res.-Atmos., 118, 7922–7937, https://doi.org/10.1002/jgrd.50376,
2013. a
Chen, X., Huang, X., and Flanner, M. G.: Sensitivity of modeled far-IR
radiation budgets in polar continents to treatments of snow surface and ice
cloud radiative properties, Geophys. Res. Lett., 41, 6530–6537,
https://doi.org/10.1002/2014GL061216,
2014. a
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J. L., Minnis, P.,
Stubenrauch, C. J., and Zeng, S.: The GCM-Oriented CALIPSO Cloud Product
(CALIPSO-GOCCP), J. Geophys. Res., 115, D00H16,
https://doi.org/10.1029/2009JD012251, 2010. a, b
Cheruy, F., Dufresne, J. L., Hourdin, F., and Ducharne, A.: Role of clouds and
land-atmosphere coupling in midlatitude continental summer warm biases and
climate change amplification in CMIP5 simulations, Geophys. Res. Lett., 41, 6493–6500, https://doi.org/10.1002/2014GL061145, 2014. a
Choulga, M., Kourzeneva, E., Zakharova, E., and Doganovsky, A.: Estimation of
the mean depth of boreal lakes for use in numerical weather prediction and
climate modelling, Tellus A, 66,
21295, https://doi.org/10.3402/tellusa.v66.21295, 2014. a
Cole, J.: Figures for CanAM5 paper – v1.0.0 (v1.0.0), Zenodo [code],
https://doi.org/10.5281/zenodo.7579680, 2023. a
Comer, N. T., Lafleur, P. M., Roulet, N. T., Letts, M. G., Skarupa, M., and
Verseghy, D.: A test of the Canadian land surface scheme (class) for a
variety of wetland types, Atmosphere-Ocean, 38, 161–179,
https://doi.org/10.1080/07055900.2000.9649644,
2000. a
Côté, J. and Konrad, J.-M.: A generalized thermal conductivity model for
soils and construction materials, Can. Geotech. J., 42,
443–458, https://doi.org/10.1139/t04-106, 2005. a
Dingman, S., L.: Physical hydrology, Prentice-Hall, Upper Saddle River, NJ,
USA, 2nd edn., ISBN 9780130996954, 2002. a
Ebert, E. E. and Curry, J. A.: An intermediate one-dimensional thermodynamic
sea ice model for investigating ice-atmosphere interactions, J.
Geophys. Res.-Oceans, 98, 10085–10109, https://doi.org/10.1029/93JC00656, 1993. a
ECMWF: IFS Documentation CY25R1 – Part IV: Physical Processes, in:
IFS Documentation CY25R1, edited by: White, P., no. 4 in IFS
Documentation, ECMWF, https://doi.org/10.21957/6hswlclmt,
2003. 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, c
Farmer, D. M. and Carmack, E.: Wind Mixing and Restratification in a Lake near
the Temperature of Maximum Density, J. Phys. Oceanogr., 11,
1516–1533, https://doi.org/10.1175/1520-0485(1981)011<1516:WMARIA>2.0.CO;2, 1981. a
Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to
the treatment of ice thermodynamics and dynamics, J. Geophys.
Res.-Oceans, 102, 12609–12646,
https://doi.org/10.1029/97JC00480,
1997. a
Flanner, M. G., Liu, X., Zhou, C., Penner, J. E., and Jiao, C.: Enhanced solar energy absorption by internally-mixed black carbon in snow grains, Atmos. Chem. Phys., 12, 4699–4721, https://doi.org/10.5194/acp-12-4699-2012, 2012. a
Frouin, R., Iacobellis, S. F., and Deschamps, P.-Y.: Influence of oceanic
whitecaps on the Global Radiation Budget, Geophys. Res. Lett., 28,
1523–1526, https://doi.org/10.1029/2000GL012657, 2001. a
Garratt, J. R.: The atmospheric boundary layer, Cambridge University Press
Cambridge, New York, New York, USA, ISBN 0-521-38052-9, 1992. a
Gettelman, A., Morrison, H., Santos, S., Bogenschutz, P., and Caldwell, P. M.:
Advanced Two-Moment Bulk Microphysics for Global Models. Part II: Global
Model Solutions and Aerosol–Cloud Interactions, J. Climate, 28,
1288–1307, https://doi.org/10.1175/JCLI-D-14-00103.1
2015. a
Ghan, S. J., Smith, S. J., Wang, M., Zhang, K., Pringle, K., Carslaw, K.,
Pierce, J., Bauer, S., and Adams, P.: A simple model of global aerosol
indirect effects, J. Geophys. Res.-Atmos., 118,
6688–6707, https://doi.org/10.1002/jgrd.50567,
2013. a
Hedstrom, N. R. and Pomeroy, J. W.: Measurements and modelling of snow
interception in the boreal forest, Hydrol. Process., 12, 1611–1625,
https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4,
1998. a, b
Hersbach, H., Bell, B., Berriford, P., Berrisford, G., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers,
D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly data
on pressure levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS),
https://doi.org/10.24381/cds.6860a573, 2019a. a
Hersbach, H., Bell, B., Berriford, P., Berrisford, G., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers,
D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly data
on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.f17050d7,
2019b. 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
Hourdin, F., Williamson, D., Rio, C., Couvreux, F., Roehrig, R., Villefranque,
N., Musat, I., Fairhead, L., Diallo, F. B., and Volodina, V.: Process-Based
Climate Model Development Harnessing Machine Learning: II. Model
Calibration From Single Column to Global, J. Adv. Model.
Earth Sy., 13, e2020MS002225,
https://doi.org/10.1029/2020MS002225, 2021. a
Imberger, J.: The diurnal mixed layer1, Limnol. Oceanogr., 30,
737–770, https://doi.org/10.4319/lo.1985.30.4.0737,
1985. a
Jin, Z., Qiao, Y., Wang, Y., Fang, Y., and Yi, W.: A new parameterization of
spectral and broadband ocean surface albedo, Opt. Express, 19,
26429–26443, https://doi.org/10.1364/OE.19.026429,
2011. a
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X., Smith, W. L., Su, W., and Ham, S.-H.: Surface Irradiances
of Edition 4.0 Clouds and the Earth’s Radiant Energy System (CERES)
Energy Balanced and Filled (EBAF) Data Product, J. Climate, 31,
4501–4527, https://doi.org/10.1175/JCLI-D-17-0523.1, 2018. a, b
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
Knapp, K. R., Young, A. H., Semunegus, H., Inamdar, A. K., and Hankins, W.:
Adjusting ISCCP Cloud Detection to Increase Consistency of Cloud Amount and
Reduce Artifacts, J. Atmos. Ocean. Tech., 38, 155–165, https://doi.org/10.1175/JTECH-D-20-0045.1, 2021. a, b
Kourzeneva, E., Asensio, H., Martin, E., and Faroux, S.: Global gridded dataset
of lake coverage and lake depth for use in numerical weather prediction and
climate modelling, Tellus A, 64,
15640, https://doi.org/10.3402/tellusa.v64i0.15640, 2012. a
Lalande, M., Ménégoz, M., Krinner, G., Naegeli, K., and Wunderle, S.: Climate change in the High Mountain Asia in CMIP6, Earth Syst. Dynam., 12, 1061–1098, https://doi.org/10.5194/esd-12-1061-2021, 2021. a
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land
surface in the Community Land Model (CLM 3.0), J. Geophys.
Res.-Biogeo., 112, G01023, https://doi.org/10.1029/2006JG000168, 2007. a, b, c
Lebsock, M., Morrison, H., and Gettelman, A.: Microphysical implications of
cloud-precipitation covariance derived from satellite remote sensing, J. Geophys. Res.-Atmos., 118, 6521–6533,
https://doi.org/10.1002/jgrd.50347,
2013. a
Lee, T. J. and Pielke, R. A.: Estimating the Soil Surface Specific
Humidity, J. Appl. Meteorol. Climatol., 31, 480–484,
https://doi.org/10.1175/1520-0450(1992)031<0480:ETSSSH>2.0.CO;2, 1992. a
Leppäranta, M. and Wang, K.: The ice cover on small and large lakes:
scaling analysis and mathematical modelling, Hydrobiologia, 599, 183–189,
https://doi.org/10.1007/s10750-007-9201-3, 2008. a
Letts, M. G., Roulet, N. T., Comer, N. T., Skarupa, M. R., and Verseghy, D. L.:
Parametrization of peatland hydraulic properties for the Canadian land
surface scheme, Atmosphere-Ocean, 38, 141–160,
https://doi.org/10.1080/07055900.2000.9649643,
2000. a
Li, J., von Salzen, K., Peng, Y., Zhang, H., and Liang, X.: Evaluation of black
carbon semi‒direct radiative effect in a climate model, J. Geophys. Res.-Atmos., 118, 4715–4728,
https://doi.org/10.1002/jgrd.50327,
2013. a
Liu, Y. and Daum, P. H.: Parameterization of the Autoconversion Process.Part I:
Analytical Formulation of the Kessler-Type Parameterizations, J.
Atmos. Sci., 61, 1539–1548,
https://doi.org/10.1175/1520-0469(2004)061<1539:POTAPI>2.0.CO;2,
2004. a
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G.,
Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth’s
Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)
Top-of-Atmosphere (TOA) Edition-4.0 Data Product, J. Climate, 31,
895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018. a, b
Ma, X., von Salzen, K., and Li, J.: Modelling sea salt aerosol and its direct and indirect effects on climate, Atmos. Chem. Phys., 8, 1311–1327, https://doi.org/10.5194/acp-8-1311-2008, 2008. a
MacKay, M. D.: A Process-Oriented Small Lake Scheme for Coupled Climate
Modeling Applications, J. Hydrometeorol., 13, 1911–1924,
https://doi.org/10.1175/JHM-D-11-0116.1,
2012. a
MacKay, M. D., Verseghy, D. L., Fortin, V., and Rennie, M. D.: Wintertime
Simulations of a Boreal Lake with the Canadian Small Lake Model, J.
Hydrometeorol., 18, 2143–2160, https://doi.org/10.1175/JHM-D-16-0268.1,
2017. a
Matthes, K., Funke, B., Andersson, M. E., Barnard, L., Beer, J., Charbonneau, P., Clilverd, M. A., Dudok de Wit, T., Haberreiter, M., Hendry, A., Jackman, C. H., Kretzschmar, M., Kruschke, T., Kunze, M., Langematz, U., Marsh, D. R., Maycock, A. C., Misios, S., Rodger, C. J., Scaife, A. A., Seppälä, A., Shangguan, M., Sinnhuber, M., Tourpali, K., Usoskin, I., van de Kamp, M., Verronen, P. T., and Versick, S.: Solar forcing for CMIP6 (v3.2), Geosci. Model Dev., 10, 2247–2302, https://doi.org/10.5194/gmd-10-2247-2017, 2017. a
McLandress, C., Scinocca, J. F., Shepherd, T. G., Reader, M. C., and Manney,
G. L.: Dynamical Control of the Mesosphere by Orographic and Nonorographic
Gravity Wave Drag during the Extended Northern Winters of 2006 and 2009,
J. Atmos. Sci., 70, 2152–2169,
https://doi.org/10.1175/JAS-D-12-0297.1, 2013. a
Michibata, T., Suzuki, K., Sekiguchi, M., and Takemura, T.: Prognostic
Precipitation in the MIROC6-SPRINTARS GCM: Description and Evaluation Against
Satellite Observations, J. Adv. Model. Earth Sy., 11,
839–860, https://doi.org/10.1029/2018MS001596,
2019. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., and Iacono, M. J.: Radiative
transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k
model for the longwave, J. Geophys. Res., 102, 16663–16682, 1997. a
Morcrette, C. J., Van Weverberg, K., Ma, H.-Y., Ahlgrimm, M., Bazile, E., Berg,
L. K., Cheng, A., Cheruy, F., Cole, J., Forbes, R., Gustafson Jr, W. I.,
Huang, M., Lee, W.-S., Liu, Y., Mellul, L., Merryfield, W. J., Qian, Y.,
Roehrig, R., Wang, Y.-C., Xie, S., Xu, K.-M., Zhang, C., Klein, S., and
Petch, J.: Introduction to CAUSES: Description of Weather and Climate
Models and Their Near-Surface Temperature Errors in 5 day Hindcasts Near the
Southern Great Plains, J. Geophys. Res.-Atmos., 123,
2655–2683, https://doi.org/10.1002/2017JD027199,
2018. a
Namazi, M., von Salzen, K., and Cole, J. N. S.: Simulation of black carbon in snow and its climate impact in the Canadian Global Climate Model, Atmos. Chem. Phys., 15, 10887–10904, https://doi.org/10.5194/acp-15-10887-2015, 2015. a, b, c
NASA/LARC/SD/ASDC: International Satellite Cloud Climatology Project (ISCCP) Stage D2 Monthly Cloud Products - Revised Algorithm in Hierarchical Data Format, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/ISCCP/D2, 1999. a, b
NASA/LARC/SD/ASDC: CERES Energy Balanced and Filled (EBAF) TOA Monthly means data in netCDF Edition4.1, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/TERRA-AQUA/CERES/EBAF-TOA_L3B004.1, 2019a. a
NASA/LARC/SD/ASDC: CERES Energy Balanced and Filled (EBAF) TOA and Surface Monthly means data in netCDF Edition 4.1, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/TERRA-AQUA/CERES/EBAF_L3B.004.1, 2019b. a
Niiler, P. and Kraus, E. B.: One-dimensional models of the upper ocean, in:
Modelling and prediction of the upper layers of the ocean, edited by: Kraus,
E., 143–172, Pergamon Press, 1977. a
Oleson, W. B., Lawrence, M., Bonan, B., Flanner, G., Kluzek, E., Lawrence, J.,
Levis, S., Swenson, C. L., Thornton, E., Dai, A., Decker, M., Dickinson,
R. E., Feddema, J. J., Heald, L., Hoffman, F. M., Lamarque, J.-F., Mahowald,
N. M., Niu, G., Qian, T., Randerson, J. T., Running, S. W., Sakaguchi, K.,
Slater, A., Stöckli, R., Wang, A., Yang, Z.-L., Zeng, X., and Zeng, X.:
Technical Description of version 4.0 of the Community Land Model
(CLM), https://doi.org/10.5065/D6FB50WZ, 2010. a
Peng, Y., von Salzen, K., and Li, J.: Simulation of mineral dust aerosol with Piecewise Log-normal Approximation (PLA) in CanAM4-PAM, Atmos. Chem. Phys., 12, 6891–6914, https://doi.org/10.5194/acp-12-6891-2012, 2012. a, b
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Hofmann, R.
J. P.: Reconciling Simulated and Observed Views of Clouds: MODIS, ISCCP,
and the Limits of Instrument Simulators, J. Climate, 25, 4699–4720, https://doi.org/10.1175/JCLI-D-11-00267.1, 2012. a, b
Pincus, R., Mlawer, E. J., Oreopoulos, L., Ackerman, A. S., Baek, S., Brath,
M., Buehler, S. A., Cady-Pereira, K. E., Cole, J. N. S., Dufresne, J.-L.,
Kelley, M., Li, J., Manners, J., Paynter, D. J., Roehrig, R., Sekiguchi, M.,
and Schwarzkopf, D. M.: Radiative flux and forcing parameterization error in
aerosol-free clear skies, Geophys. Res. Lett., 42, 5485–5492,
https://doi.org/10.1002/2015GL064291, 2015. a, b
Pincus, R., Forster, P. M., and Stevens, B.: The Radiative Forcing Model Intercomparison Project (RFMIP): experimental protocol for CMIP6, Geosci. Model Dev., 9, 3447–3460, https://doi.org/10.5194/gmd-9-3447-2016, 2016. a
Pomeroy, J. W. and Gray, D. M.: Snowcover : accumulation,
relocation, and management, Tech. Rep. 7, National Hydrology Research
Institute (Canada),, Saskatoon, Saskatchewan,
http://publications.gc.ca/pub?id=9.892773&sl=0 (last access: 14 September 2023), 1995. a
Rayner, K. N.: Diurnal energetics of a reservoir surface layer, Environ. Dyn.
Rep. ED-80-005, University of Western Australia, 1980. a
Rossow, W. B., Walker, A., Golea, V., Knapp, K. R., Young, A., Inamdar, A. K.,
Hankins, B., and NOAA's Climate Data Record Program: International Satellite Cloud
Climatology Project Climate Data Record, H-Series HGG NOAA
National Centers for Environmental Information,
https://doi.org/10.7289/V5QZ281S, 2016. a
Rothman, L. S., Gordon, I. E., Babikov, Y., Barbe, A., Benner, D. C., Bernath,
P. F., Birk, M., Bizzocchi, L., Boudon, V., Brown, L. R., Campargue, A.,
Chance, K., Cohen, E. A., Coudert, L. H., Devi, V. M., Drouin, B. J., Fayt,
A., Flaud, J.-M., Gamache, R. R., Harrison, J. J., Hartmann, J.-M., Hill, C.,
Hodges, J. T., Jacquemart, D., Jolly, A., Lamouroux, J., Roy, R. J. L., Li,
G., Long, D. A., Lyulin, O. M., Mackie, C. J., Massie, S. T., Mikhailenko,
S., Müller, H. S. P., Naumenko, O. V., Nikitin, A. V., Orphal, J.,
Perevalov, V., Perrin, A., Polovtseva, E. R., Richard, C., Smith, M. A. H.,
Starikova, E., Sung, K., Tashkun, S., Tennyson, J., Toon, G. C., Tyuterev,
V. G., and Wagner, G.: The HITRAN2012 molecular spectroscopic database,
J. Quant. Spectrosc. Ra., 130, 4–50,
https://doi.org/10.1016/j.jqsrt.2013.07.002,
2013. a
Sant, V., Posselt, R., and Lohmann, U.: Prognostic precipitation with three liquid water classes in the ECHAM5–HAM GCM, Atmos. Chem. Phys., 15, 8717–8738, https://doi.org/10.5194/acp-15-8717-2015, 2015. a
Schmidt, R. A. and Gluns, D. R.: Snowfall interception on branches of three
conifer species, Can. J. Forest Res., 21, 1262–1269,
https://doi.org/10.1139/x91-176, 1991. a
Scinocca, J. F. and McFarlane, N. A.: The parametrization of drag induced by
stratified flow over anisotropic orography, Q. J. Roy. Meteor. Soc., 126, 2353–2393,
https://doi.org/10.1002/qj.49712656802, 2000. a
Scinocca, J. F., McFarlane, N. A., Lazare, M., Li, J., and Plummer, D.: Technical Note: The CCCma third generation AGCM and its extension into the middle atmosphere, Atmos. Chem. Phys., 8, 7055–7074, https://doi.org/10.5194/acp-8-7055-2008, 2008. a, b
Smith, C. J., Kramer, R. J., Myhre, G., Alterskjær, K., Collins, W., Sima, A., Boucher, O., Dufresne, J.-L., Nabat, P., Michou, M., Yukimoto, S., Cole, J., Paynter, D., Shiogama, H., O'Connor, F. M., Robertson, E., Wiltshire, A., Andrews, T., Hannay, C., Miller, R., Nazarenko, L., Kirkevåg, A., Olivié, D., Fiedler, S., Lewinschal, A., Mackallah, C., Dix, M., Pincus, R., and Forster, P. M.: Effective radiative forcing and adjustments in CMIP6 models, Atmos. Chem. Phys., 20, 9591–9618, https://doi.org/10.5194/acp-20-9591-2020, 2020. a
Soulis, E. D., Craig, J. R., Fortin, V., and Liu, G.: A simple expression for
the bulk field capacity of a sloping soil horizon, Hydrol. Process.,
25, 112–116, https://doi.org/10.1002/hyp.7827, 2011. a
Spigel, R. H., Imberger, J., and Rayner, K. N.: Modeling the diurnal mixed
layer, Limnol. Oceanogr., 31, 533–556,
https://doi.org/10.4319/lo.1986.31.3.0533,
1986. a
Storelvmo, T., Tan, I., and Korolev, A. V.: Cloud Phase Changes Induced
by CO2 Warming – a Powerful yet Poorly Constrained
Cloud-Climate Feedback, Current Climate Change Reports, 1, 288–296,
https://doi.org/10.1007/s40641-015-0026-2, 2015. a
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A.,
Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen,
C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.:
Assessment of Global Cloud Datasets from Satellites: Project and Database
Initiated by the GEWEX Radiation Panel, B. Am. Meteorol. Soc., 94,
1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1,
2013. a
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal conductivity
of seasonal snow, J. Glaciol., 43, 26–41,
https://doi.org/10.3189/S0022143000002781, 1997. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019. a, b, c, d, e, f, g, h, i, j, k, l, m
Swart, N. C., Cole, J., Kharin, S., Lazare, M., Scinocca, J., Gillett, N., Anstey, J., Arora, V., Christian, J.,
Hanna, S., Jiao, Y., Lee, W., Majaess, F., Saenko, O., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen,
K., Yang, D., and Winter, B.: The Canadian Earth System Model (CanESM) – v5.0.3 (v5.0.3), Zenodo,
https://doi.org/10.5281/zenodo.3251114, 2023. a
Tabler, R., Benson, C., Santana, B., and Ganguly, P.: Estimating snow
transport from wind speed records: estimates versus measurements at Prudhoe
Bay, Alaska, in: 58th Annual Western Snow Conference, Proceedings
of the 58th Annual Western Snow Conference, 17–19 April 1990,
Sacramento, California, Western Snow Conference,
https://westernsnowconference.org/sites/westernsnowconference.org/PDFs/1990Tabler.pdf (last
access: 14 September 2023), 1990. 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, 2011. a
Tesdal, J.-E., Christian, J. R., Monahan, A. H., and von Salzen, K.: Evaluation
of diverse approaches for estimating sea-surface DMS concentration and
air-sea exchange at global scale, Environ. Chem., 13, 390–412,
https://doi.org/10.1071/EN14255, 2016a. a
Tesdal, J.-E., Christian, J. R., Monahan, A. H., and von Salzen, K.: Sensitivity of modelled sulfate aerosol and its radiative effect on climate to ocean DMS concentration and air–sea flux, Atmos. Chem. Phys., 16, 10847–10864, https://doi.org/10.5194/acp-16-10847-2016, 2016b. a
Verseghy, D. L.: CLASS: A Canadian land surface scheme for GCMS. I. Soil model,
Int. J. Climatol., 11, 111–133,
https://doi.org/10.1002/joc.3370110202, 1991. a
Verseghy, D. L., McFarlane, N. A., and Lazare, M.: A Canadian land surface
scheme for GCMs: II Vegetation model and coupled runs., Int. J. Climatol., 13,
347–370, 1993. a
Virgin, J. G., Fletcher, C. G., Cole, J. N. S., von Salzen, K., and Mitovski, T.: Cloud Feedbacks from CanESM2 to CanESM5.0 and their influence on climate sensitivity, Geosci. Model Dev., 14, 5355–5372, https://doi.org/10.5194/gmd-14-5355-2021, 2021. a
von Salzen, K., Scinocca, J. F., McFarlane, N. A., Li, J., Cole, J. N. S.,
Plummer, D., Verseghy, D., Reader, M. C., Ma, X., Lazare, M., and Solheim,
L.: The Canadian Fourth Generation Atmospheric Global Climate Model (CanAM4).
Part I: Representation of Physical Processes, Atmosphere-Ocean, 51, 104–125,
https://doi.org/10.1080/07055900.2012.755610,
2013. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Webb, M. J., Andrews, T., Bodas-Salcedo, A., Bony, S., Bretherton, C. S., Chadwick, R., Chepfer, H., Douville, H., Good, P., Kay, J. E., Klein, S. A., Marchand, R., Medeiros, B., Siebesma, A. P., Skinner, C. B., Stevens, B., Tselioudis, G., Tsushima, Y., and Watanabe, M.: The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6, Geosci. Model Dev., 10, 359–384, https://doi.org/10.5194/gmd-10-359-2017, 2017. a
Wild, M.: The global energy balance as represented in CMIP6 climate models,
Clim. Dynam., 55, 553–577, https://doi.org/10.1007/s00382-020-05282-7, 2020. a
Wood, R.: Drizzle in Stratiform Boundary Layer Clouds. Part II: Microphysical
Aspects, J. Atmos. Sci., 62, 3034–3050,
https://doi.org/10.1175/JAS3530.1,
2005.
a, b, c, d
Wu, K., Li, J., von Salzen, K., and Zhang, F.: Explicit solutions to the mixing
rules with three-component inclusions, J. Quant. Spectrosc. Ra., 207, 78–82,
https://doi.org/10.1016/j.jqsrt.2017.12.020,
2018. a
Yang, P., Bi, L., Baum, B. A., Liou, K.-N., Kattawar, G. W., Mishchenko, M. I.,
and Cole, B.: Spectrally Consistent Scattering, Absorption, and Polarization
Properties of Atmospheric Ice Crystals at Wavelengths from 0.2 to 100 µm,
J. Atmos. Sci., 70, 330–347,
https://doi.org/10.1175/JAS-D-12-039.1, 2012. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M.,
Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate
Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47,
e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
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
Zhang, Y., Carey, S. K., and Quinton, W. L.: Evaluation of the algorithms and
parameterizations for ground thawing and freezing simulation in permafrost
regions, J. Geophys. Res.-Atmos., 113, D17116,
https://doi.org/10.1029/2007JD009343, 2008. a
Zhao, L. and Gray, D. M.: A parametric expression for estimating infiltration
into frozen soils, Hydrol. Process., 11, 1761–1775,
https://doi.org/10.1002/(SICI)1099-1085(19971030)11:13<1761::AID-HYP604>3.0.CO;2-O,
1997. a
Zhou, C., Zelinka, M. D., and Klein, S. A.: Analyzing the dependence of global
cloud feedback on the spatial pattern of sea surface temperature change with
a Green's function approach, J. Adv. Model. Earth Sy.,
9, 2174–2189, https://doi.org/10.1002/2017MS001096, 2017. a
Zhou, T., Turner, A. G., Kinter, J. L., Wang, B., Qian, Y., Chen, X., Wu, B., Wang, B., Liu, B., Zou, L., and He, B.: GMMIP (v1.0) contribution to CMIP6: Global Monsoons Model Inter-comparison Project, Geosci. Model Dev., 9, 3589–3604, https://doi.org/10.5194/gmd-9-3589-2016, 2016. a
Zobler, L.: A World Soil File for Global Climate Modelling, Tech.
Rep. NASA Technical Memorandum 87802, NASA Goddard Institute for Space
Studies, New York, New York, USA, 1986. a
Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the...
Special issue