Articles | Volume 25, issue 18
https://doi.org/10.5194/acp-25-10523-2025
© Author(s) 2025. 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-25-10523-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2025
Research article |
| 16 Sep 2025
| 16 Sep 2025
Strong intermodel differences and biases in CMIP6 simulations of PM2.5, aerosol optical depth, and precipitation over Africa
Catherine A. Toolan
CORRESPONDING AUTHOR
Department of Meteorology, University of Reading, Reading, United Kingdom
Joe Adabouk Amooli
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, United States of America
Laura J. Wilcox
National Centre for Atmospheric Science, University of Reading, Reading, United Kingdom
Bjørn H. Samset
Center for International Climate and Environmental Research (CICERO), Oslo, Norway
Andrew G. Turner
Department of Meteorology, University of Reading, Reading, United Kingdom
National Centre for Atmospheric Science, University of Reading, Reading, United Kingdom
Daniel M. Westervelt
Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY, United States of America
NASA Goddard Institute for Space Studies, New York, NY, United States of America
Related authors
No articles found.
Carley Elizabeth Iles, Bjørn Hallvard Samset, and Marianne Tronstad Lund
EGUsphere, https://doi.org/10.5194/egusphere-2025-4115, https://doi.org/10.5194/egusphere-2025-4115, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Polar sea ice changes and midlatitude weather affect each other, but how these teleconnections play out differ between the poles and between sea ice regions. Knowing how they interact is important for climate risk assessments, but few studies have investigated how the teleconnections evolve with global warming. Using large ensembles of climate model simulations, we find teleconnections patterns that differ between sea ice regions, but are quite robust to changes in global surface temperature.
Paul T. Griffiths, Laura J. Wilcox, Robert J. Allen, Vaishali Naik, Fiona M. O'Connor, Michael Prather, Alex Archibald, Florence Brown, Makoto Deushi, William Collins, Stephanie Fiedler, Naga Oshima, Lee T. Murray, Bjørn H. Samset, Chris Smith, Steven Turnock, Duncan Watson-Parris, and Paul J. Young
Atmos. Chem. Phys., 25, 8289–8328, https://doi.org/10.5194/acp-25-8289-2025, https://doi.org/10.5194/acp-25-8289-2025, 2025
Short summary
Short summary
The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) aimed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. We review its contribution to AR6 (Sixth Assessment Report of the Intergovernmental Panel on Climate Change) and the wider understanding of the role of these species in climate and climate change. We identify challenges and provide recommendations to improve the utility and uptake of climate model data, detailed summary tables of CMIP6 models, experiments, and emergent diagnostics.
Tuomas Naakka, Daniel Köhler, Kalle Nordling, Petri Räisänen, Marianne Tronstad Lund, Risto Makkonen, Joonas Merikanto, Bjørn H. Samset, Victoria A. Sinclair, Jennie L. Thomas, and Annica M. L. Ekman
Atmos. Chem. Phys., 25, 8127–8145, https://doi.org/10.5194/acp-25-8127-2025, https://doi.org/10.5194/acp-25-8127-2025, 2025
Short summary
Short summary
The effects of warmer sea surface temperatures and decreasing sea ice cover on polar climates have been studied using four climate models with identical prescribed changes in sea surface temperatures and sea ice cover. The models predict similar changes in air temperature and precipitation in the polar regions in a warmer climate with less sea ice. However, the models disagree on how the atmospheric circulation, i.e. the large-scale winds, will change with warmer temperatures and less sea ice.
Feifei Luo, Bjørn H. Samset, Camilla W. Stjern, Manoj Joshi, Laura J. Wilcox, Robert J. Allen, Wei Hua, and Shuanglin Li
Atmos. Chem. Phys., 25, 7647–7667, https://doi.org/10.5194/acp-25-7647-2025, https://doi.org/10.5194/acp-25-7647-2025, 2025
Short summary
Short summary
Black carbon (BC) aerosol is emitted from the incomplete combustion of biomass and fossil fuels. We found that Asian BC leads to strong local cooling and drying. Reductions in precipitation primarily depend on the thermodynamic effects due to solar radiation absorption by BC. The combined thermodynamic and dynamic effects shape the spatial pattern of precipitation responses to Asian BC. These results help us further understand the impact of emissions of anthropogenic aerosols on Asian climate.
Beth Dingley, James A. Anstey, Marta Abalos, Carsten Abraham, Tommi Bergman, Lisa Bock, Sonya Fiddes, Birgit Hassler, Ryan J. Kramer, Fei Luo, Fiona M. O'Connor, Petr Šácha, Isla R. Simpson, Laura J. Wilcox, and Mark D. Zelinka
EGUsphere, https://doi.org/10.5194/egusphere-2025-3189, https://doi.org/10.5194/egusphere-2025-3189, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This manuscript defines as a list of variables and scientific opportunities which are requested from the CMIP7 Assessment Fast Track to address open atmospheric science questions. The list reflects the output of a large public community engagement effort, coordinated across autumn 2025 through to summer 2025.
Wah Kin Michael Lai, Jon Robson, Laura Wilcox, Nick Dunstone, and Rowan Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2025-2598, https://doi.org/10.5194/egusphere-2025-2598, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
In a climate model at two different resolutions, anthropogenic aerosols induce a fast cooling followed by a delayed warming in the subpolar North Atlantic. The delayed warming is stronger at higher resolution due to a stronger Atlantic Meridional Overturning Circulation (AMOC) response. This difference is due to the lower resolution model having more sea ice which insulates the ocean. This result show that the North Atlantic response to external forcing is sensitive to regional differences.
Gopalakrishna Pillai Gopikrishnan, Daniel M. Westervelt, and Jayanarayanan Kuttippurath
EGUsphere, https://doi.org/10.5194/egusphere-2025-1056, https://doi.org/10.5194/egusphere-2025-1056, 2025
Short summary
Short summary
This study examines the inverse effect of aerosol surface area and particulate matter (PM) reduction on air quality and atmospheric chemistry, particularly on surface ozone levels. Aerosols act as surfaces for the uptake of hydroxyl radicals (HO2), which are essential for controlling ozone formation. Reducing aerosols and PM may enhance surface ozone formation, thus worsening air quality. However, further efforts to decrease NOx emissions could mitigate this rise in surface ozone levels.
Duncan Watson-Parris, Laura J. Wilcox, Camilla W. Stjern, Robert J. Allen, Geeta Persad, Massimo A. Bollasina, Annica M. L. Ekman, Carley E. Iles, Manoj Joshi, Marianne T. Lund, Daniel McCoy, Daniel M. Westervelt, Andrew I. L. Williams, and Bjørn H. Samset
Atmos. Chem. Phys., 25, 4443–4454, https://doi.org/10.5194/acp-25-4443-2025, https://doi.org/10.5194/acp-25-4443-2025, 2025
Short summary
Short summary
In 2020, regulations by the International Maritime Organization aimed to reduce aerosol emissions from ships. These aerosols previously had a cooling effect, which the regulations might reduce, revealing more greenhouse gas warming. Here we find that, while there is regional warming, the global 2020–2040 temperature rise is only +0.03 °C. This small change is difficult to distinguish from natural climate variability, indicating the regulations have had a limited effect on observed warming to date.
Joe Adabouk Amooli, Marianne T. Lund, Sourangsu Chowdhury, Gunnar Myhre, Ane N. Johansen, Bjørn H. Samset, and Daniel M. Westervelt
EGUsphere, https://doi.org/10.5194/egusphere-2025-948, https://doi.org/10.5194/egusphere-2025-948, 2025
Short summary
Short summary
We analyze various projections of African aerosol emissions and their potential impacts on climate and public health. We find that future emissions vary widely across emission projections, with differences in sectoral emission distributions. Using the Oslo chemical transport model, we show that air pollution exposure in some regions of Africa could increase significantly by 2050, increasing pollution-related deaths, with most scenarios projecting aerosol-induced warming over sub-Saharan Africa.
Kalle Nordling, Nora L. S. Fahrenbach, and Bjørn H. Samset
Atmos. Chem. Phys., 25, 1659–1684, https://doi.org/10.5194/acp-25-1659-2025, https://doi.org/10.5194/acp-25-1659-2025, 2025
Short summary
Short summary
People experience daily weather, not changes in monthly averages. We investigate the likelihood of events, which occurred once every 10 years in the pre-industrial era. We analyze how summertime precipitation and daily maximum temperature events evolve. Our focus is on understanding the role of day-to-day variability in the change in the number of extreme weather days. We find that in most regions, a change in variability is the primary driver for change in summertime extreme precipitation.
Kieran M. R. Hunt, Jean-Philippe Baudouin, Andrew G. Turner, A. P. Dimri, Ghulam Jeelani, Pooja, Rajib Chattopadhyay, Forest Cannon, T. Arulalan, M. S. Shekhar, T. P. Sabin, and Eliza Palazzi
Weather Clim. Dynam., 6, 43–112, https://doi.org/10.5194/wcd-6-43-2025, https://doi.org/10.5194/wcd-6-43-2025, 2025
Short summary
Short summary
Western disturbances (WDs) are storms that predominantly affect north India and Pakistan during the winter months, where they play an important role in regional water security, but can also bring a range of natural hazards. In this review, we summarise recent literature across a range of topics: their structure and lifecycle, precipitation and impacts, interactions with large-scale weather patterns, representation in models, how well they are forecast, and their response to changes in climate.
Alcide Zhao, Laura J. Wilcox, and Claire L. Ryder
Atmos. Chem. Phys., 24, 13385–13402, https://doi.org/10.5194/acp-24-13385-2024, https://doi.org/10.5194/acp-24-13385-2024, 2024
Short summary
Short summary
Climate models include desert dust aerosols, which cause atmospheric heating and can change circulation patterns. We assess the effect of dust on the Indian and east Asian summer monsoons through multi-model experiments isolating the effect of dust in current climate models for the first time. Dust atmospheric heating results in a southward shift of western Pacific equatorial rainfall and an enhanced Indian summer monsoon. This shows the importance of accurate dust representation in models.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
Short summary
Short summary
We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Robert J. Allen, Xueying Zhao, Cynthia A. Randles, Ryan J. Kramer, Bjørn H. Samset, and Christopher J. Smith
Atmos. Chem. Phys., 24, 11207–11226, https://doi.org/10.5194/acp-24-11207-2024, https://doi.org/10.5194/acp-24-11207-2024, 2024
Short summary
Short summary
Present-day methane shortwave absorption mutes 28% (7–55%) of the surface warming associated with its longwave absorption. The precipitation increase associated with the longwave radiative effects of the present-day methane perturbation is also muted by shortwave absorption but not significantly so. Methane shortwave absorption also impacts the magnitude of its climate feedback parameter, largely through the cloud feedback.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
Short summary
Short summary
The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Zhen Liu, Massimo A. Bollasina, and Laura J. Wilcox
Atmos. Chem. Phys., 24, 7227–7252, https://doi.org/10.5194/acp-24-7227-2024, https://doi.org/10.5194/acp-24-7227-2024, 2024
Short summary
Short summary
The aerosol impact on monsoon precipitation and circulation is strongly influenced by a model-simulated spatio-temporal variability in the climatological monsoon precipitation across Asia, which critically modulates the efficacy of aerosol–cloud–precipitation interactions, the predominant driver of the total aerosol response. There is a strong interplay between South Asia and East Asia monsoon precipitation biases and their relative predominance in driving the overall monsoon response.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
Short summary
Short summary
The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Saroj Kumar Sahu, Poonam Mangaraj, Gufran Beig, Marianne T. Lund, Bjørn Hallvard Samset, Pallavi Sahoo, and Ashirbad Mishra
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-310, https://doi.org/10.5194/essd-2023-310, 2023
Revised manuscript not accepted
Short summary
Short summary
Elevated emission of particulate matter is not limited to urban areas, led to poor air quality across the country. Emission Inventory is the first line of defensive tools for air quality management and understanding and identification of the source of pollutants. The present work is an attempt to develop a high-resolution (~10 km) national inventory of particulate pollutants in India for 2020 using IPCC methodology. The developed dataset is vital piece of information for mitigation strategies.
Giorgia Di Capua, Dim Coumou, Bart van den Hurk, Antje Weisheimer, Andrew G. Turner, and Reik V. Donner
Weather Clim. Dynam., 4, 701–723, https://doi.org/10.5194/wcd-4-701-2023, https://doi.org/10.5194/wcd-4-701-2023, 2023
Short summary
Short summary
Heavy rainfall in tropical regions interacts with mid-latitude circulation patterns, and this interaction can explain weather patterns in the Northern Hemisphere during summer. In this analysis we detect these tropical–extratropical interaction pattern both in observational datasets and data obtained by atmospheric models and assess how well atmospheric models can reproduce the observed patterns. We find a good agreement although these relationships are weaker in model data.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
Short summary
Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Marianne Tronstad Lund, Gunnar Myhre, Ragnhild Bieltvedt Skeie, Bjørn Hallvard Samset, and Zbigniew Klimont
Atmos. Chem. Phys., 23, 6647–6662, https://doi.org/10.5194/acp-23-6647-2023, https://doi.org/10.5194/acp-23-6647-2023, 2023
Short summary
Short summary
Here we show that differences, in magnitude and trend, between recent global anthropogenic emission inventories have a notable influence on simulated regional abundances of anthropogenic aerosol over the 1990–2019 period. This, in turn, affects estimates of radiative forcing. Our findings form a basis for comparing existing and upcoming studies on anthropogenic aerosols using different emission inventories.
Forwood Wiser, Bryan K. Place, Siddhartha Sen, Havala O. T. Pye, Benjamin Yang, Daniel M. Westervelt, Daven K. Henze, Arlene M. Fiore, and V. Faye McNeill
Geosci. Model Dev., 16, 1801–1821, https://doi.org/10.5194/gmd-16-1801-2023, https://doi.org/10.5194/gmd-16-1801-2023, 2023
Short summary
Short summary
We developed a reduced model of atmospheric isoprene oxidation, AMORE-Isoprene 1.0. It was created using a new Automated Model Reduction (AMORE) method designed to simplify complex chemical mechanisms with minimal manual adjustments to the output. AMORE-Isoprene 1.0 has improved accuracy and similar size to other reduced isoprene mechanisms. When included in the CRACMM mechanism, it improved the accuracy of EPA’s CMAQ model predictions for the northeastern USA compared to observations.
Jarmo S. Kikstra, Zebedee R. J. Nicholls, Christopher J. Smith, Jared Lewis, Robin D. Lamboll, Edward Byers, Marit Sandstad, Malte Meinshausen, Matthew J. Gidden, Joeri Rogelj, Elmar Kriegler, Glen P. Peters, Jan S. Fuglestvedt, Ragnhild B. Skeie, Bjørn H. Samset, Laura Wienpahl, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Alaa Al Khourdajie, Piers M. Forster, Andy Reisinger, Roberto Schaeffer, and Keywan Riahi
Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, https://doi.org/10.5194/gmd-15-9075-2022, 2022
Short summary
Short summary
Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source
climate-assessmentworkflow that was used in the IPCC AR6 Working Group III report. The paper provides key insight for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement.
Kieran M. R. Hunt and Andrew G. Turner
Weather Clim. Dynam., 3, 1341–1358, https://doi.org/10.5194/wcd-3-1341-2022, https://doi.org/10.5194/wcd-3-1341-2022, 2022
Short summary
Short summary
More than half of India's summer monsoon rainfall arises from low-pressure systems: storms originating over the Bay of Bengal. In observation-based data, we examine how the generation and pathway of these storms are changed by the
boreal summer intraseasonal oscillation– the chief means of large-scale control on the monsoon at timescales of a few weeks. Our study offers new insights for useful prediction of these storms, important for both water resources planning and disaster early warning.
Ambrogio Volonté, Andrew G. Turner, Reinhard Schiemann, Pier Luigi Vidale, and Nicholas P. Klingaman
Weather Clim. Dynam., 3, 575–599, https://doi.org/10.5194/wcd-3-575-2022, https://doi.org/10.5194/wcd-3-575-2022, 2022
Short summary
Short summary
In this study we analyse the complex seasonal evolution of the East Asian summer monsoon. Using reanalysis data, we show the importance of the interaction between tropical and extratropical air masses converging at the monsoon front, particularly during its northward progression. The upper-level flow pattern (e.g. the westerly jet) controls the balance between the airstreams and thus the associated rainfall. This framework provides a basis for studies of extreme events and climate variability.
Mathew Sebastian, Sobhan Kumar Kompalli, Vasudevan Anil Kumar, Sandhya Jose, S. Suresh Babu, Govindan Pandithurai, Sachchidanand Singh, Rakesh K. Hooda, Vijay K. Soni, Jeffrey R. Pierce, Ville Vakkari, Eija Asmi, Daniel M. Westervelt, Antti-Pekka Hyvärinen, and Vijay P. Kanawade
Atmos. Chem. Phys., 22, 4491–4508, https://doi.org/10.5194/acp-22-4491-2022, https://doi.org/10.5194/acp-22-4491-2022, 2022
Short summary
Short summary
Characteristics of particle number size distributions and new particle formation in six locations in India were analyzed. New particle formation occurred frequently during the pre-monsoon (spring) season and it significantly modulates the shape of the particle number size distributions. The contribution of newly formed particles to cloud condensation nuclei concentrations was ~3 times higher in urban locations than in mountain background locations.
Alcide Zhao, Claire L. Ryder, and Laura J. Wilcox
Atmos. Chem. Phys., 22, 2095–2119, https://doi.org/10.5194/acp-22-2095-2022, https://doi.org/10.5194/acp-22-2095-2022, 2022
Short summary
Short summary
The CMIP6 models' simulated dust processes are getting more uncertain as models become more sophisticated. Of particular challenge are the links between dust cycles and optical properties, and we recommend more detailed output relating to dust cycles in future intercomparison projects to constrain such links. Also, models struggle to capture certain key regional dust processes such as dust accumulation along the slope of the Himalayas and dust seasonal cycles in North China and North America.
Jie Zhang, Kalli Furtado, Steven T. Turnock, Jane P. Mulcahy, Laura J. Wilcox, Ben B. Booth, David Sexton, Tongwen Wu, Fang Zhang, and Qianxia Liu
Atmos. Chem. Phys., 21, 18609–18627, https://doi.org/10.5194/acp-21-18609-2021, https://doi.org/10.5194/acp-21-18609-2021, 2021
Short summary
Short summary
The CMIP6 ESMs systematically underestimate TAS anomalies in the NH midlatitudes, especially from 1960 to 1990. The anomalous cooling is concurrent in time and space with anthropogenic SO2 emissions. The spurious drop in TAS is attributed to the overestimated aerosol concentrations. The aerosol forcing sensitivity cannot well explain the inter-model spread of PHC biases. And the cloud-amount term accounts for most of the inter-model spread in aerosol forcing sensitivity.
Mark R. Muetzelfeldt, Reinhard Schiemann, Andrew G. Turner, Nicholas P. Klingaman, Pier Luigi Vidale, and Malcolm J. Roberts
Hydrol. Earth Syst. Sci., 25, 6381–6405, https://doi.org/10.5194/hess-25-6381-2021, https://doi.org/10.5194/hess-25-6381-2021, 2021
Short summary
Short summary
Simulating East Asian Summer Monsoon (EASM) rainfall poses many challenges because of its multi-scale nature. We evaluate three setups of a 14 km global climate model against observations to see if they improve simulated rainfall. We do this over catchment basins of different sizes to estimate how model performance depends on spatial scale. Using explicit convection improves rainfall diurnal cycle, yet more model tuning is needed to improve mean and intensity biases in simulated summer rainfall.
Maria Sand, Bjørn H. Samset, Gunnar Myhre, Jonas Gliß, Susanne E. Bauer, Huisheng Bian, Mian Chin, Ramiro Checa-Garcia, Paul Ginoux, Zak Kipling, Alf Kirkevåg, Harri Kokkola, Philippe Le Sager, Marianne T. Lund, Hitoshi Matsui, Twan van Noije, Dirk J. L. Olivié, Samuel Remy, Michael Schulz, Philip Stier, Camilla W. Stjern, Toshihiko Takemura, Kostas Tsigaridis, Svetlana G. Tsyro, and Duncan Watson-Parris
Atmos. Chem. Phys., 21, 15929–15947, https://doi.org/10.5194/acp-21-15929-2021, https://doi.org/10.5194/acp-21-15929-2021, 2021
Short summary
Short summary
Absorption of shortwave radiation by aerosols can modify precipitation and clouds but is poorly constrained in models. A total of 15 different aerosol models from AeroCom phase III have reported total aerosol absorption, and for the first time, 11 of these models have reported in a consistent experiment the contributions to absorption from black carbon, dust, and organic aerosol. Here, we document the model diversity in aerosol absorption.
Liang Guo, Laura J. Wilcox, Massimo Bollasina, Steven T. Turnock, Marianne T. Lund, and Lixia Zhang
Atmos. Chem. Phys., 21, 15299–15308, https://doi.org/10.5194/acp-21-15299-2021, https://doi.org/10.5194/acp-21-15299-2021, 2021
Short summary
Short summary
Severe haze remains serious over Beijing despite emissions decreasing since 2008. Future haze changes in four scenarios are studied. The pattern conducive to haze weather increases with the atmospheric warming caused by the accumulation of greenhouse gases. However, the actual haze intensity, measured by either PM2.5 or optical depth, decreases with aerosol emissions. We show that only using the weather pattern index to predict the future change of Beijing haze is insufficient.
Kalle Nordling, Hannele Korhonen, Jouni Räisänen, Antti-Ilari Partanen, Bjørn H. Samset, and Joonas Merikanto
Atmos. Chem. Phys., 21, 14941–14958, https://doi.org/10.5194/acp-21-14941-2021, https://doi.org/10.5194/acp-21-14941-2021, 2021
Short summary
Short summary
Understanding the temperature responses to different climate forcing agents, such as greenhouse gases and aerosols, is crucial for understanding future regional climate changes. In climate models, the regional temperature responses vary for all forcing agents, but the causes of this variability are poorly understood. For all forcing agents, the main component contributing to variance in regional surface temperature responses between the climate models is the clear-sky longwave emissivity.
Tao Tang, Drew Shindell, Yuqiang Zhang, Apostolos Voulgarakis, Jean-Francois Lamarque, Gunnar Myhre, Gregory Faluvegi, Bjørn H. Samset, Timothy Andrews, Dirk Olivié, Toshihiko Takemura, and Xuhui Lee
Atmos. Chem. Phys., 21, 13797–13809, https://doi.org/10.5194/acp-21-13797-2021, https://doi.org/10.5194/acp-21-13797-2021, 2021
Short summary
Short summary
Previous studies showed that black carbon (BC) could warm the surface with decreased incoming radiation. With climate models, we found that the surface energy redistribution plays a more crucial role in surface temperature compared with other forcing agents. Though BC could reduce the surface heating, the energy dissipates less efficiently, which is manifested by reduced convective and evaporative cooling, thereby warming the surface.
Robin D. Lamboll, Chris D. Jones, Ragnhild B. Skeie, Stephanie Fiedler, Bjørn H. Samset, Nathan P. Gillett, Joeri Rogelj, and Piers M. Forster
Geosci. Model Dev., 14, 3683–3695, https://doi.org/10.5194/gmd-14-3683-2021, https://doi.org/10.5194/gmd-14-3683-2021, 2021
Short summary
Short summary
Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions. We can estimate the changes in emissions at a country level, but to make predictions about how this will affect our climate, we need more precise information about where the emissions happen. Here we combine older estimates of where emissions normally occur with very recent estimates of sector activity levels to enable different groups to make simulations of the climatic effects of lockdown.
Lixia Zhang, Laura J. Wilcox, Nick J. Dunstone, David J. Paynter, Shuai Hu, Massimo Bollasina, Donghuan Li, Jonathan K. P. Shonk, and Liwei Zou
Atmos. Chem. Phys., 21, 7499–7514, https://doi.org/10.5194/acp-21-7499-2021, https://doi.org/10.5194/acp-21-7499-2021, 2021
Short summary
Short summary
The projected frequency of circulation patterns associated with haze events and global warming increases significantly due to weakening of the East Asian winter monsoon. Rapid reduction in anthropogenic aerosol further increases the frequency of circulation patterns, but haze events are less dangerous. We revealed competing effects of aerosol emission reductions on future haze events through their direct contribution to haze intensity and their influence on the atmospheric circulation patterns.
Daniel M. Westervelt, Arlene M. Fiore, Colleen B. Baublitz, and Gustavo Correa
Atmos. Chem. Phys., 21, 6799–6810, https://doi.org/10.5194/acp-21-6799-2021, https://doi.org/10.5194/acp-21-6799-2021, 2021
Short summary
Short summary
Particulate air pollution in the atmosphere can impact the availability of gas-phase chemical constituents, which can then have feedbacks on gas-phase air pollutants. We use a chemistry–climate computer model to simulate the impact of particulate pollution from three major world regions on gas-phase chemical constituents. We find that surface-level ozone air pollution decreases by up to 5 ppbv over China in response to Chinese particulate air pollution, which has implications for policy.
Sazzad Hossain, Hannah L. Cloke, Andrea Ficchì, Andrew G. Turner, and Elisabeth M. Stephens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-97, https://doi.org/10.5194/hess-2021-97, 2021
Manuscript not accepted for further review
Short summary
Short summary
Hydrometeorological drivers are investigated to study three different flood types: long duration, rapid rise and high water level of the Brahmaputra river basin in Bangladesh. Our results reveal that long duration floods have been driven by basin-wide rainfall whereas rapid rate of rise due to more localized rainfall. We find that recent record high water levels are not coincident with extreme river flows. Understanding these drivers is key for flood forecasting and early warning.
Jonathan K. P. Shonk, Andrew G. Turner, Amulya Chevuturi, Laura J. Wilcox, Andrea J. Dittus, and Ed Hawkins
Atmos. Chem. Phys., 20, 14903–14915, https://doi.org/10.5194/acp-20-14903-2020, https://doi.org/10.5194/acp-20-14903-2020, 2020
Short summary
Short summary
We use a set of model simulations of the 20th century to demonstrate that the uncertainty in the cooling effect of man-made aerosol emissions has a wide range of impacts on global monsoons. For the weakest cooling, the impact of aerosol is overpowered by greenhouse gas (GHG) warming and monsoon rainfall increases in the late 20th century. For the strongest cooling, aerosol impact dominates over GHG warming, leading to reduced monsoon rainfall, particularly from 1950 to 1980.
Liang Guo, Ruud J. van der Ent, Nicholas P. Klingaman, Marie-Estelle Demory, Pier Luigi Vidale, Andrew G. Turner, Claudia C. Stephan, and Amulya Chevuturi
Geosci. Model Dev., 13, 6011–6028, https://doi.org/10.5194/gmd-13-6011-2020, https://doi.org/10.5194/gmd-13-6011-2020, 2020
Short summary
Short summary
Precipitation over East Asia simulated in the Met Office Unified Model is compared with observations. Moisture sources of EA precipitation are traced using a moisture tracking model. Biases in moisture sources are linked to biases in precipitation. Using the tracking model, changes in moisture sources can be attributed to changes in SST, circulation and associated evaporation. This proves that the method used in this study is useful to identify the causes of biases in regional precipitation.
Camilla W. Stjern, Bjørn H. Samset, Olivier Boucher, Trond Iversen, Jean-François Lamarque, Gunnar Myhre, Drew Shindell, and Toshihiko Takemura
Atmos. Chem. Phys., 20, 13467–13480, https://doi.org/10.5194/acp-20-13467-2020, https://doi.org/10.5194/acp-20-13467-2020, 2020
Short summary
Short summary
The span between the warmest and coldest temperatures over a day is a climate parameter that influences both agriculture and human health. Using data from 10 models, we show how individual climate drivers such as greenhouse gases and aerosols produce distinctly different responses in this parameter in high-emission regions. Given the high uncertainty in future aerosol emissions, this improved understanding of the temperature responses may ultimately help these regions prepare for future changes.
Marianne T. Lund, Borgar Aamaas, Camilla W. Stjern, Zbigniew Klimont, Terje K. Berntsen, and Bjørn H. Samset
Earth Syst. Dynam., 11, 977–993, https://doi.org/10.5194/esd-11-977-2020, https://doi.org/10.5194/esd-11-977-2020, 2020
Short summary
Short summary
Achieving the Paris Agreement temperature goals requires both near-zero levels of long-lived greenhouse gases and deep cuts in emissions of short-lived climate forcers (SLCFs). Here we quantify the near- and long-term global temperature impacts of emissions of individual SLCFs and CO2 from 7 economic sectors in 13 regions in order to provide the detailed knowledge needed to design efficient mitigation strategies at the sectoral and regional levels.
Zebedee R. J. Nicholls, Malte Meinshausen, Jared Lewis, Robert Gieseke, Dietmar Dommenget, Kalyn Dorheim, Chen-Shuo Fan, Jan S. Fuglestvedt, Thomas Gasser, Ulrich Golüke, Philip Goodwin, Corinne Hartin, Austin P. Hope, Elmar Kriegler, Nicholas J. Leach, Davide Marchegiani, Laura A. McBride, Yann Quilcaille, Joeri Rogelj, Ross J. Salawitch, Bjørn H. Samset, Marit Sandstad, Alexey N. Shiklomanov, Ragnhild B. Skeie, Christopher J. Smith, Steve Smith, Katsumasa Tanaka, Junichi Tsutsui, and Zhiang Xie
Geosci. Model Dev., 13, 5175–5190, https://doi.org/10.5194/gmd-13-5175-2020, https://doi.org/10.5194/gmd-13-5175-2020, 2020
Short summary
Short summary
Computational limits mean that we cannot run our most comprehensive climate models for all applications of interest. In such cases, reduced complexity models (RCMs) are used. Here, researchers working on 15 different models present the first systematic community effort to evaluate and compare RCMs: the Reduced Complexity Model Intercomparison Project (RCMIP). Our research ensures that users of RCMs can more easily evaluate the strengths, weaknesses and limitations of their tools.
Laura J. Wilcox, Zhen Liu, Bjørn H. Samset, Ed Hawkins, Marianne T. Lund, Kalle Nordling, Sabine Undorf, Massimo Bollasina, Annica M. L. Ekman, Srinath Krishnan, Joonas Merikanto, and Andrew G. Turner
Atmos. Chem. Phys., 20, 11955–11977, https://doi.org/10.5194/acp-20-11955-2020, https://doi.org/10.5194/acp-20-11955-2020, 2020
Short summary
Short summary
Projected changes in man-made aerosol range from large reductions to moderate increases in emissions until 2050. Rapid reductions between the present and the 2050s lead to enhanced increases in global and Asian summer monsoon precipitation relative to scenarios with continued increases in aerosol. Relative magnitude and spatial distribution of aerosol changes are particularly important for South Asian summer monsoon precipitation changes, affecting the sign of the trend in the coming decades.
Giorgia Di Capua, Jakob Runge, Reik V. Donner, Bart van den Hurk, Andrew G. Turner, Ramesh Vellore, Raghavan Krishnan, and Dim Coumou
Weather Clim. Dynam., 1, 519–539, https://doi.org/10.5194/wcd-1-519-2020, https://doi.org/10.5194/wcd-1-519-2020, 2020
Short summary
Short summary
We study the interactions between the tropical convective activity and the mid-latitude circulation in the Northern Hemisphere during boreal summer. We identify two circumglobal wave patterns with phase shifts corresponding to the South Asian and the western North Pacific monsoon systems at an intra-seasonal timescale. These patterns show two-way interactions in a causal framework at a weekly timescale and assess how El Niño affects these interactions.
Cited articles
AERONET: Aerosol Robotic Network (AERONET) Homepage, https://aeronet.gsfc.nasa.gov (last access: 29 March 2024), 2024. a
AirNow: US Embassies and Consulates, https://www.airnow.gov/international/us-embassies-and-consulates/ (last access: 29 March 2024), 2021. a
AirNow: Embassy Monitoring Site Data, https://www.arcgis.com/home/item.html?id=db1a45b351164464ac0459a52890e5a0 last access: 21 August 2025. a
Andrews, M. B., Ridley, J. K., Wood, R. A., Andrews, T., Blockley, E. W., Booth, B., Burke, E., Dittus, A. J., Florek, P., Gray, L. J., Haddad, S., Hardiman, S. C., Hermanson, L., Hodson, D., Hogan, E., Jones, G. S., Knight, J. R., Kuhlbrodt, T., Misios, S., Mizielinski, M. S., Ringer, M. A., Robson, J., and Sutton, R. T.: Historical Simulations With HadGEM3-GC3.1 for CMIP6, J. Adv. Model. Earth Sy., 12, e2019MS001995, https://doi.org/10.1029/2019MS001995, 2020. a, b
Annor, T., Ackon, A. T., James, R., Dyer, E., Webb, T., Pokam, W. M., Kuete Gouandjo, G., Washington, R., and Abiodun, B. J.: Heat band, rain band and heat low migration: process-based evaluation of some CMIP6 GCMs over West Africa, Clim. Dynam., 62, 791–806, https://doi.org/10.1007/s00382-023-06930-4, 2023. a
Anuforom, A. C.: Spatial distribution and temporal variability of Harmattan dust haze in sub-Sahel West Africa, Atmos. Environ., 41, 9079–9090, 2007. a
Archer-Nicholls, S., Allen, R., Abraham, N. L., Griffiths, P. T., and Archibald, A. T.: Large simulated future changes in the nitrate radical under the CMIP6 SSP scenarios: implications for oxidation chemistry, Atmos. Chem. Phys., 23, 5801–5813, https://doi.org/10.5194/acp-23-5801-2023, 2023. a
Ayehu, G. T., Tadesse, T., Gessesse, B., and Dinku, T.: Validation of new satellite rainfall products over the Upper Blue Nile Basin, Ethiopia, Atmos. Meas. Tech., 11, 1921–1936, https://doi.org/10.5194/amt-11-1921-2018, 2018. a
Ayugi, B., Zhihong, J., Zhu, H., Ngoma, H., Babaousmail, H., Rizwan, K., and Dike, V.: Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa, Int. J. Climatol., 41, 6474–6496, 2021. a
Ayugi, B. O., Chung, E.-S., Babaousmail, H., and Lim Kam Sian, K. T. C.: Characterizing the performances of different observational precipitation products and their uncertainties over Africa, Environ. Res. Lett., 19, 064009, https://doi.org/10.1088/1748-9326/ad416b, 2024. a
Bader, D. C., Leung, R., Taylor, M., and McCoy, R. B.: E3SM-Project E3SM1.0 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4497, 2019a. a
Bader, D. C., Leung, R., Taylor, M., and McCoy, R. B.: E3SM-Project E3SM1.1 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.11485, 2019b. a
Bader, D. C., Leung, R., Taylor, M., and McCoy, R. B.: E3SM-Project E3SM1.1ECA model output prepared for CMIP6 CMIP historical, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/AR6.C6CMEPE1, 2020. a
Bader, D. C., Leung, R., Taylor, M., and McCoy, R. B.: E3SM-Project E3SM2.0NARRM model output prepared for CMIP6 CMIP amip, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.17501, 2023. a
Bauer, S. E., Im, U., Mezuman, K., and Gao, C. Y.: Desert dust, industrialization and agricultural fires: Health impacts of outdoor air pollution in Africa, J. Geophys. Res.-Atmos., 124, 4104–4120, https://doi.org/10.1029/2018JD029336, 2019. a
Beck, H. E., Vergopolan, N., Pan, M., Levizzani, V., van Dijk, A. I. J. M., Weedon, G. P., Brocca, L., Pappenberger, F., Huffman, G. J., and Wood, E. F.: Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling, Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, 2017. a
Bentsen, M., Oliviè, D. J. L., Seland, y., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-MM model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8040, 2019. a
Bethke, I., Wang, Y., Counillon, F., Kimmritz, M., Fransner, F., Samuelsen, A., Langehaug, H. R., Chiu, P.-G., Bentsen, M., Guo, C., Tjiputra, J., Kirkevåg, A., Oliviè, D. J. L., Seland, y., Fan, Y., Lawrence, P., Eldevik, T., and Keenlyside, N.: NCC NorCPM1 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10894, 2019. a, b
Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., Meehl, G. A., Predoi, V., Roberts, M. J., and Eyring, V.: Quantifying Progress Across Different CMIP Phases With the ESMValTool, J. Geophys. Res.-Atmos., 125, e2019JD032321, https://doi.org/10.1029/2019jd032321, 2020. a, b
Booyens, W., Beukes, J. P., Van Zyl, P. G., Ruiz-Jimenez, J., Kopperi, M., Riekkola, M.-L., Josipovic, M., Vakkari, V., and Laakso, L.: Summary of research paper published in Journal of Atmospheric Chemistry titled: Assessment of polar organic aerosols at a regional background site in southern Africa, Clean Air J., 29, 89–113, https://doi.org/10.17159/2410-972x/2019/v29n1a10, 2019. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Tasch, P.., Satheesh, S., Sherwood, S., Stevens, B., and Zhang, X.: Clouds and aerosols, in: 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, 571–657, https://doi.org/10.1017/CBO9781107415324.016, 2013. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton, T.: IPSL IPSL-CM5A2-INCA model output prepared for CMIP6 ScenarioMIP ssp370, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/AR6.C6SPIPICI, 2020a. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, Lionel, E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sy., 12, e2019MS002010, https://doi.org/10.1029/2019MS002010, 2020b. a, b
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Balkanski, Y., Checa-Garcia, R., Hauglustaine, D., Bekki, S., and Marchand, M.: IPSL IPSL-CM6A-LR-INCA model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.13582, 2021a. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols, M.-A., Meurdesoif, Y., Balkanski, Y., Checa-Garcia, R., Hauglustaine, D., Bekki, S., and Marchand, M.: IPSL IPSL-CM6A-LR-INCA model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.13601, 2021b. a
Burrows, S. M., Maltrud, M., Yang, X., Zhu, Q., Jeffery, N., Shi, X., Ricciuto, D., Wang, S., Bisht, G., Tang, J., Wolfe, J., Harrop, B. E., Singh, B., Brent, L., Baldwin, S., Zhou, T., Cameron-Smith, P., Keen, N., Collier, N., Xu, M., Hunke, E. C., Elliott, S. M., Turner, A. K., Li, H., Wang, H., Golaz, J.-C., Bond-Lamberty, B., Hoffman, F. M., Riley, W. J., Thornton, P. E., Calvin, K., and Leung, L. R.: The DOE E3SM v1.1 biogeochemistry configuration: Description and simulated ecosystem‐climate responses to historical changes in forcing, J. Adv. Model. Earth Syst., 12, e2019MS001766, https://doi.org/10.1029/2019MS001766, 2020. a, b
Byun, Y.-H., Lim, Y.-J., Sung, H. M., Kim, J., Sun, M., and Kim, B.-H.: NIMS-KMA KACE1.0-G model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8378, 2019. a
Chen, D., Rojas, M., Samset, B., Cobb, K., Diongue Niang, A., Edwards, P., Emori, S., Faria, S., Hawkins, E., Hope, P., Huybrechts, P., Meinshausen, M., Mustafa, S., Plattner, G.-K., and Tréguier, A.-M.: Framing, Context, and Methods, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 147–286, https://doi.org/10.1017/9781009157896.003, 2021. a
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.: Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled Model, J. Adv. Model. Earth Sy., 11, 185–209, https://doi.org/10.1029/2018MS001369, 2019. a, b
Christian, J. R., Denman, K. L., Hayashida, H., Holdsworth, A. M., Lee, W. G., Riche, O. G. J., Shao, A. E., Steiner, N., and Swart, N. C.: Ocean biogeochemistry in the Canadian Earth System Model version 5.0.3: CanESM5 and CanESM5-CanOE, Geosci. Model Dev., 15, 4393–4424, https://doi.org/10.5194/gmd-15-4393-2022, 2022. a
Cinquini, L., Crichton, D., Mattmann, C., Harney, J., Shipman, G., Wang, F., Ananthakrishnan, R., Miller, N., Denvil, S., Morgan, M., Pobre, Z., Bell, G. M., Doutriaux, C., Drach, R., Williams, D., Kershaw, P., Pascoe, S., Gonzalez, E., Fiore, S., and Schweitzer, R.: The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data, Future Generation Computer Systems, 36, 400–417, https://doi.org/10.1016/j.future.2013.07.002, 2014. a
Cook, K. H.: Generation of the African Easterly Jet and Its Role in Determining West African Precipitation, J. Climate, 12, 1165–1184, https://doi.org/10.1175/1520-0442(1999)012<1165:GOTAEJ>2.0.CO;2, 1999. a
Copans, J.: The Sahelian drought: social sciences and the political economy of underdevelopment, Routledge, 83–97, ISBN 9780429329579, https://doi.org/10.4324/9780429329579-5, 2019. a
Danabasoglu, G.: NCAR CESM2-FV2 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.11297, 2019a. a
Danabasoglu, G.: NCAR CESM2-WACCM model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10071, 2019b. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.7627, 2019c. a
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Sy, 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a, b, c
Danek, C., Shi, X., Stepanek, C., Yang, H., Barbi, D., Hegewald, J., and Lohmann, G.: AWI AWI-ESM1.1LR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.9328, 2020. a
Di Baldassarre, G., Montanari, A., Lins, H., Koutsoyiannis, D., Brandimarte, L., and Blöschl, G.: Flood fatalities in Africa: From diagnosis to mitigation, Geophys. Res. Lett., 37, L22402, https://doi.org/10.1029/2010gl045467, 2010. a
Diem, J. E., Sung, H. S., Konecky, B. L., Palace, M. W., Salerno, J., and Hartter, J.: Rainfall Characteristics and Trends – and the Role of Congo Westerlies – in the Western Uganda Transition Zone of Equatorial Africa From 1983 to 2017, J. Geophys. Res.-Atmos., 124, 10712–10729, https://doi.org/10.1029/2019jd031243, 2019. a
Dinku, T., Funk, C., Peterson, P., Maidment, R., Tadesse, T., Gadain, H., and Ceccato, P.: Validation of the CHIRPS satellite rainfall estimates over eastern Africa, Q. J. Roy. Meteor. Soc., 144, 292–312, https://doi.org/10.1002/qj.3244, 2018. a, b
Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah, C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A., Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M., Bodman, R., Dias, F. B., Domingues, C., Hannah, N., Heerdegen, A., Savita, A., Wales, S., Allen, C., Druken, K., Evans, B., Richards, C., Ridzwan, S. M., Roberts, D., Smillie, J., Snow, K., Ward, M., and Yang, R.: CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4271, 2019. a, b
Dixon, R. D., Daloz, A. S., Vimont, D. J., and Biasutti, M.: Saharan Heat Low Biases in CMIP5 Models, J. Climate, 30, 2867–2884, https://doi.org/10.1175/JCLI-D-16-0134.1, 2017. a
Dong, B., Sutton, R. T., Highwood, E., and Wilcox, L.: The Impacts of European and Asian Anthropogenic Sulfur Dioxide Emissions on Sahel Rainfall, J. Climate, 27, 7000–7017, https://doi.org/10.1175/jcli-d-13-00769.1, 2014. a
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., Bergmann, T., Bernadello, R., Bousetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O’Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth System Model for the Climate Model Intercomparison Project 6, Geosci. Model. Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-446, 2021. a
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022. a, b
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E., Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C., Durachta, J., Dussin, R., Gauthier, P. P. G., Griffies, S. M., Guo, H., Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R., Milly, P. C. D., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A., Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T., Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B., Zeng, Y., and Zhao, M.: The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): Overall Coupled Model Description and Simulation Characteristics, J. Adv. Model. Earth Sy., 12, e2019MS002015, https://doi.org/10.1029/2019MS002015, 2020. a
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth3-Veg model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.727, 2019a. a
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth3 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4700, 2019b. a
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth3-AerChem model output prepared for CMIP6 AerChemMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.699, 2020. a
EC-Earth Consortium (EC-Earth): EC-Earth-Consortium EC-Earth-3-CC model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4702, 2021. a
Ekolu, J., Dieppois, B., Sidibe, M., Eden, J. M., Tramblay, Y., Villarini, G., Peña-Angulo, D., Mahé, G., Paturel, J.-E., Onyutha, C., and van de Wiel, M.: Long-term variability in hydrological droughts and floods in sub-Saharan Africa: New perspectives from a 65-year daily streamflow dataset, J. Hydrol., 613, 128359, https://doi.org/10.1016/j.jhydrol.2022.128359, 2022. a
ECMWF: ERA5, https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5, last access: 21 August 2025. 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
Fiedler, S., van Noije, T., Smith, C. J., Boucher, O., Dufresne, J.-L., Kirkevåg, A., Olivié, D., Pinto, R., Reerink, T., Sima, A., and Schulz, M.: Historical changes and reasons for model differences in anthropogenic aerosol forcing in CMIP6, Geophys. Res. Lett., 50, e2023GL104848, https://doi.org/10.1029/2023GL104848, 2023. a
Fosu-Amankwah, K., Bessardon, G. E., Quansah, E., Amekudzi, L. K., Brooks, B. J., and Damoah, R.: Assessment of aerosol burden over Ghana, Sci. African, 14, e00971, https://doi.org/10.1016/j.sciaf.2021.e00971, 2021. a
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Sci. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015. a
Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Denier van der Gon, H., Facchini, M. C., Fowler, D., Koren, I., Langford, B., Lohmann, U., Nemitz, E., Pandis, S., Riipinen, I., Rudich, Y., Schaap, M., Slowik, J. G., Spracklen, D. V., Vignati, E., Wild, M., Williams, M., and Gilardoni, S.: Particulate matter, air quality and climate: lessons learned and future needs, Atmos. Chem. Phys., 15, 8217–8299, https://doi.org/10.5194/acp-15-8217-2015, 2015. a
Garrigues, S., Remy, S., Chimot, J., Ades, M., Inness, A., Flemming, J., Kipling, Z., Laszlo, I., Benedetti, A., Ribas, R., Jafariserajehlou, S., Fougnie, B., Kondragunta, S., Engelen, R., Peuch, V.-H., Parrington, M., Bousserez, N., Vazquez Navarro, M., and Agusti-Panareda, A.: Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system, Atmos. Chem. Phys., 22, 14657–14692, https://doi.org/10.5194/acp-22-14657-2022, 2022. a, b
Gidden, M., Riahi, K., Smith, S., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: input4MIPs.CMIP6.ScenarioMIP.IAMC, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/INPUT4MIPS.10464, 2018. a
GISS, N.: NASA-GISS GISS-E2.1G model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.7127, 2018. a
GISS, N.: NASA-GISS GISS-E2-1-G-CC model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.11762, 2019a. a
GISS, N.: NASA-GISS GISS-E2.1H model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.7128, 2019b. a
Glantz, M. H. (Ed.): Politics of natural disaster, Praeger special studies in international economics and development, Praeger, Westport, CT, ISBN 0275011801, 1976. a
Golaz, J.-C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q., Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C., Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M. A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S., Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J. G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J., Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W., Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H.-Y., Lin, W., Lipscomb, W. H., Ma, P.-L., Mahajan, S., Maltrud, M. E., Mametjanov, A., McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch, P. J., Reeves Eyre, J. E. J., Riley, W. J., Ringler, T. D., Roberts, A. F., Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X., Singh, B., Tang, J., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan, H., Wang, H., Wang, S., Williams, D. N., Wolfram, P. J., Worley, P. H., Xie, S., Yang, Y., Yoon, J.-H., Zelinka, M. D., Zender, C. S., Zeng, X., Zhang, C., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.: The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard Resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, https://doi.org/10.1029/2018MS001603, 2019. a
Golaz, J.-C., Van Roekel, L. P., Zheng, X., Roberts, A. F., Wolfe, J. D., Lin, W., Bradley, A. M., Tang, Q., Maltrud, M. E., Forsyth, R. M., Zhang, C., Zhou, T., Zhang, K., Zender, C. S., Wu, M., Wang, H., Turner, A. K., Singh, B., Richter, J. H., Qin, Y., Petersen, M. R., Mametjanov, A., Ma, P.-L., Larson, V. E., Krishna, J., Keen, N. D., Jeffery, N., Hunke, E. C., Hannah, W. M., Guba, O., Griffin, B. M., Feng, Y., Engwirda, D., Di Vittorio, A. V., Dang, C., Conlon, L. M., Chen, C.-C.-J., Brunke, M. A., Bisht, G., Benedict, J. J., Asay-Davis, X. S., Zhang, Y., Zhang, M., Zeng, X., Xie, S., Wolfram, P. J., Vo, T., Veneziani, M., Tesfa, T. K., Sreepathi, S., Salinger, A. G., Reeves Eyre, J. E. J., Prather, M. J., Mahajan, S., Li, Q., Jones, P. W., Jacob, R. L., Huebler, G. W., Huang, X., Hillman, B. R., Harrop, B. E., Foucar, J. G., Fang, Y., Comeau, D. S., Caldwell, P. M., Bartoletti, T., Balaguru, K., Taylor, M. A., McCoy, R. B., Leung, L. R., and Bader, D. C.: The DOE E3SM model version 2: Overview of the physical model and initial model evaluation, J. Adv. Model. Earth Syst., 14, e2022MS003156, https://doi.org/10.1029/2022MS003156, 2022. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005. a
Guo, H., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Bushuk, M., Dunne, K. A., Dussin, R., Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Lin, P., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Shao, A., Silvers, L., Wyman, B., Yan, X., Zeng, Y., Adcroft, A., Dunne, J. P., Held, I. M., Krasting, J. P., Horowitz, L. W., Milly, P., Shevliakova, E., Winton, M., Zhao, M., and Zhang, R.: NOAA-GFDL GFDL-CM4 model output historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8594, 2018. a
Guo, L., Wilcox, L. J., Bollasina, M., Turnock, S. T., Lund, M. T., and Zhang, L.: Competing effects of aerosol reductions and circulation changes for future improvements in Beijing haze, Atmos. Chem. Phys., 21, 15299–15308, https://doi.org/10.5194/acp-21-15299-2021, 2021. a
Gupta, A. K., Deshmukh, A., Waman, D., Patade, S., Jadav, A., Phillips, V. T. J., Bansemer, A., Martins, J. A., and Gonçalves, F. L. T.: The microphysics of the warm-rain and ice crystal processes of precipitation in simulated continental convective storms, Commun. Earth Environ., 4, 226, https://doi.org/10.1038/s43247-023-00884-5, 2023. a
Hagler, G., Hanley, T., Hassett-Sipple, B., Vanderpool, R., Smith, M., Wilbur, J., Wilbur, T., Oliver, T., Shand, D., Vidacek, V., Johnson, C., Allen, R., and D’Angelo, C.: Evaluation of two collocated federal equivalent method PM2.5 instruments over a wide range of concentrations in Sarajevo, Bosnia and Herzegovina, Atmos. Pollut. Res., 13, 101374, https://doi.org/10.1016/j.apr.2022.101374, 2022. a
Hajima, T., Abe, M., Arakawa, O., Suzuki, T., Komuro, Y., Ogura, T., Ogochi, K., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Ohgaito, R., Ito, A., Yamazaki, D., Ito, A., Takata, K., Watanabe, S., Kawamiya, M., and Tachiiri, K.: MIROC MIROC-ES2L model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5602, 2019. a
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020. a
Hastenrath, S., Polzin, D., and Mutai, C.: Circulation Mechanisms of Kenya Rainfall Anomalies, J. Climate, 24, 404–412, https://doi.org/10.1175/2010jcli3599.1, 2011. a
He, B., Yu, Y., Bao, Q., Lin, P., Liu, H., Li, J., Wang, L., Liu, Y., Wu, G., Chen, K., Guo, Y., Zhao, S., Zhang, X., Song, M., and Xie, J.: CAS FGOALS-f3-L model dataset descriptions for CMIP6 DECK experiments, Atmos. Ocean. Sci. Lett., 13, 582–588, https://doi.org/10.1080/16742834.2020.1778419, 2020. a
Health Effects Institute: The State of Air Quality and Health Impacts in Africa | State of Global Air, https://www.stateofglobalair.org/resources/report/state-air-quality-and-health-impacts-africa (last access: 28 March 2024), 2022. a
Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T., Wyman, B., Xiang, B., Zhang, R., Anderson, W., Balaji, V., Donner, L., Dunne, K., Durachta, J., Gauthier, P. P. G., Ginoux, P., Golaz, J.-C., Griffies, S. M., Hallberg, R., Harris, L., Harrison, M., Hurlin, W., John, J., Lin, P., Lin, S.-J., Malyshev, S., Menzel, R., Milly, P. C. D., Ming, Y., Naik, V., Paynter, D., Paulot, F., Rammaswamy, V., Reichl, B., Robinson, T., Rosati, A., Seman, C., Silvers, L. G., Underwood, S., and Zadeh, N.: Structure and Performance of GFDL's CM4.0 Climate Model, J. Adv. Model. Earth Sy., 11, 3691–3727, https://doi.org/10.1029/2019MS001829, 2019. 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 Jean-Noël Thépaut: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020. a
Huang, W.: THU CIESM model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8843, 2019. a
Huffman, G. J., Adler, R. F., Behrangi, A., Bolvin, D. T., Nelkin, E. J., Gu, G., and Ehsani, M. R.: The new Version 3.2 Global Precipitation Climatology Project (GPCP) monthly and daily precipitation products, J. Climate, 36, 7635–7655, 2023. a
Hulme, M.: Climatic perspectives on Sahelian desiccation: 1973–1998, Global Environ. Chang., 11, 19–29, https://doi.org/10.1016/s0959-3780(00)00042-x, 2001. a, b
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019. a, b
Kalisa, W., Zhang, J., Igbawua, T., Henchiri, M., Mulinga, N., Nibagwire, D., and Umuhoza, M.: Spatial and temporal heterogeneity of air pollution in East Africa, Sci. Total Environ., 886, 163734, https://doi.org/10.1016/j.scitotenv.2023.163734, 2023. a, b
Kalisoras, A., Georgoulias, A. K., Akritidis, D., Allen, R. J., Naik, V., Kuo, C., Szopa, S., Nabat, P., Olivié, D., van Noije, T., Le Sager, P., Neubauer, D., Oshima, N., Mulcahy, J., Horowitz, L. W., and Zanis, P.: Decomposing the effective radiative forcing of anthropogenic aerosols based on CMIP6 Earth system models, Atmos. Chem. Phys., 24, 7837–7872, https://doi.org/10.5194/acp-24-7837-2024, 2024. a
Kapsomenakis, J., Zerefos, C., Langerock, B., Errera, Q., Basart, S., Cuevas, E., Bennouna, Y., Thouret, V., Arola, A., Pitkänen, M., Blechschmidt, A.-M., Richter, A., Eskes, H., Tsikerdekis, T., Benedictow, A., Schulz, M., Bouarar, I., and Warneke, T.: Validation report of the CAMS global Reanalysis of aerosols and reactive gases, years 2003–2021 | Copernicus, https://atmosphere.copernicus.eu/node/789 (last access: 29 March 2024), 2021. a
Karypidou, M. C., Katragkou, E., and Sobolowski, S. P.: Precipitation over southern Africa: is there consensus among global climate models (GCMs), regional climate models (RCMs) and observational data?, Geosci. Model Dev., 15, 3387–3404, https://doi.org/10.5194/gmd-15-3387-2022, 2022. a
Katoto, P. D., Byamungu, L., Brand, A. S., Mokaya, J., Strijdom, H., Goswami, N., De Boever, P., Nawrot, T. S., and Nemery, B.: Ambient air pollution and health in Sub-Saharan Africa: Current evidence, perspectives and a call to action, Environ. Res., 173, 174–188, https://doi.org/10.1016/j.envres.2019.03.029, 2019. a
Kawamiya, M., Hajima, T., Tachiiri, K., Watanabe, S., and Yokohata, T.: Two decades of Earth system modeling with an emphasis on Model for Interdisciplinary Research on Climate (MIROC), Prog. Earth Planet. Sci., 7, 64, https://doi.org/10.1186/s40645-020-00369-5, 2020. a
Kebede, A., Friberg, J., Isaxon, C., Jerrett, M., Malmqvist, E., Sjöström, C., Taj, T., and Vargas, A.: Air Quality in Africa: Public Health Implications, Ann. Rev. Publ. Health, 41, 193–210, https://doi.org/10.1146/annurev-publhealth100119-113802, 2021. a
Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C., García-Pando, C. P., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: GISS-E2.1: Configurations and Climatology, J. Adv. Model. Earth Sy., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020. a
Kim, Y., Noh, Y., Kim, D., Lee, M.-I., Lee, H. J., Kim, S. Y., and Kim, D.: KIOST KIOST-ESM model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5296, 2019. a
Krasting, J. P., John, J. G., Blanton, C., McHugh, C., Nikonov, S., Radhakrishnan, A., Rand, K., Zadeh, N. T., Balaji, V., Durachta, J., Dupuis, C., Menzel, R., Robinson, T., Underwood, S., Vahlenkamp, H., Dunne, K. A., Gauthier, P. P., Ginoux, P., Griffies, S. M., Hallberg, R., Harrison, M., Hurlin, W., Malyshev, S., Naik, V., Paulot, F., Paynter, D. J., Ploshay, J., Reichl, B. G., Schwarzkopf, D. M., Seman, C. J., Silvers, L., Wyman, B., Zeng, Y., Adcroft, A., Dunne, J. P., Dussin, R., Guo, H., He, J., Held, I. M., Horowitz, L. W., Lin, P., Milly, P., Shevliakova, E., Stock, C., Winton, M., Wittenberg, A. T., Xie, Y., and Zhao, M.: NOAA-GFDL GFDL-ESM4 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8597, 2018. a
Krishnan, R., Swapna, P., Choudhury, A. D., Narayansetti, S., Prajeesh, A. G., Singh, M., Modi, A., Mathew, R., Vellore, R., Jyoti, J., Sabin, T. P., Sanjay, J., and Ingle, S.: The IITM Earth System Model (IITM ESM), arXiv [preprint] https://doi.org/10.48550/arXiv.2101.03410, 2021. a
Kuhlbrodt, T., Jones, C. G., Sellar, A., Storkey, D., Blockley, E., Stringer, M., Hill, R., Graham, T., Ridley, J., Blaker, A., Calvert, D., Copsey, D., Ellis, R., Hewitt, H., Hyder, P., Ineson, S., Mulcahy, J., Siahaan, A., and Walton, J.: The Low-Resolution Version of HadGEM3 GC3.1: Development and Evaluation for Global Climate, J. Adv. Model. Earth Sy., 10, 2865–2888, https://doi.org/10.1029/2018MS001370, 2018. a
Lavaysse, C., Flamant, C., Evan, A., Janicot, S., and Gaetani, M.: Recent climatological trend of the Saharan heat low and its impact on the West African climate, Clim. Dynam., 47, 3479–3498, 2016. a
Lawrence, B. N., Bennett, V., Churchill, J., Juckes, M., Kershaw, P., Oliver, P., Pritchard, M., and Stephens, A.: The JASMIN super-data-cluster, arxiv [preprint], https://doi.org/10.48550/arxiv.1204.3553, 2012. a
Lee, J., Hsu, N. C., Bettenhausen, C., and Sayer, A. M.: Retrieval of aerosol optical depth under thin cirrus from MODIS: Application to an ocean algorithm, J. Geophys. Res.-Atmos., 118, 10111–10124, https://doi.org/10.1002/jgrd.50806, 2013. a
Lee, J., Kim, J., Sun, M.-A., Kim, B.-H., Moon, H., Sung, H. M., Kim, J., and Byun, Y.-H.: Evaluation of the Korea Meteorological Administration Advanced Community Earth-System model (K-ACE), Asia-Pac. J. Atmos. Sci., 56, 381–395, https://doi.org/10.1007/s13143-019-00144-7, 2020. a
Lee, W.-L. and Liang, H.-C.: IPCC DDC: AS-RCEC TaiESM1.0 model output prepared for CMIP6 CMIP, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/AR6.C6CMARTE, 2019. a
Levin, Z. and Cotton, W. R. (Eds.): Aerosol pollution impact on precipitation, Springer, New York, NY, 2009 Edn., ISBN 978-1-4020-8689-2, https://doi.org/10.1007/978-1-4020-8690-8, 2008. a
Li, L.: CAS FGOALS-g3 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3356, 2019. a
Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., Dong, L., Zhou, T., Liu, L., Wang, L., Pu, Y., Chen, X., Chen, L., Xie, Z., Liu, H., Zhang, L., Huang, X., Feng, T., Zheng, W., Xia, K., Liu, H., Liu, J., Wang, Y., Wang, L., Jia, B., Xie, F., Wang, B., Zhao, S., Yu, Z., Zhao, B., and Wei, J.: The Flexible Global Ocean-Atmosphere-Land System Model Grid-Point Version 3 (FGOALS-g3): Description and Evaluation, J. Adv. Model. Earth Sy., 12, e2019MS002012, https://doi.org/10.1029/2019MS002012, 2020. a
Lin, Y., Huang, X., Liang, Y., Qin, Y., Xu, S., Huang, W., Xu, F., Liu, L., Wang, Y., Peng, Y., Wang, L., Xue, W., Fu, H., Zhang, G. J., Wang, B., Li, R., Zhang, C., Lu, H., Yang, K., Luo, Y., Bai, Y., Song, Z., Wang, M., Zhao, W., Zhang, F., Xu, J., Zhao, X., Lu, C., Chen, Y., Luo, Y., Hu, Y., Tang, Q., Chen, D., Yang, G., and Gong, P.: Community Integrated Earth System Model (CIESM): Description and Evaluation, J. Adv. Model. Earth Sy., 12, e2019MS002036, https://doi.org/10.1029/2019MS002036, 2020. a
Lovato, T. and Peano, D.: CMCC CMCC-CM2-SR5 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3825, 2020. a
Lüdecke, H.-J., Müller-Plath, G., Wallace, M. G., and Lüning, S.: Decadal and multidecadal natural variability of African rainfall, J. Hydrol. Reg. Stud., 34, 100795, https://doi.org/10.1016/j.ejrh.2021.100795, 2021. a
Lund, M. T., Myhre, G., and Samset, B. H.: Anthropogenic aerosol forcing under the Shared Socioeconomic Pathways, Atmos. Chem. Phys., 19, 13827–13839, https://doi.org/10.5194/acp-19-13827-2019, 2019. a
Lund, M. T., Myhre, G., Skeie, R. B., Samset, B. H., and Klimont, Z.: Implications of differences between recent anthropogenic aerosol emission inventories for diagnosed AOD and radiative forcing from 1990 to 2019, Atmos. Chem. Phys., 23, 6647–6662, https://doi.org/10.5194/acp-23-6647-2023, 2023. a
McFarlane, C., Isevulambire, P. K., Lumbuenamo, R. S., Ndinga, A. M. E., Dhammapala, R., Jin, X., McNeill, V. F., Malings, C., Subramanian, R., and Westervelt, D. M.: First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors, Aerosol Air Qual. Res., 21, 200619, https://doi.org/10.4209/aaqr.200619, 2021. a
Miller, R. L., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Kelley, M., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Murray, L. T., Oinas, V., Orbe, C., Pérez García-Pando, C., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M.-S.: CMIP6 Historical Simulations (1850–2014) With GISS-E2.1, J. Adv. Model. Earth Sy., 13, e2019MS002034, https://doi.org/10.1029/2019MS002034, 2021. a, b, c
Mitchell, T. D. and Jones, P. D.: An improved method of constructing a database of monthly climate observations and associated high-resolution grids, Int. J. Climatol., 25, 693–712, 2005. a
Monerie, P., Dittus, A. J., Wilcox, L. J., and Turner, A. G.: Uncertainty in Simulating Twentieth Century West African Precipitation Trends: The Role of Anthropogenic Aerosol Emissions, Earth’s Future, 11, e2022EF002995, https://doi.org/10.1029/2022ef002995, 2023. a, b
Mulcahy, J. P., Jones, C. G., Rumbold, S. T., Kuhlbrodt, T., Dittus, A. J., Blockley, E. W., Yool, A., Walton, J., Hardacre, C., Andrews, T., Bodas-Salcedo, A., Stringer, M., de Mora, L., Harris, P., Hill, R., Kelley, D., Robertson, E., and Tang, Y.: UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model, Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, 2023. a
Munday, C., Washington, R., and Hart, N.: African Low‐Level Jets and Their Importance for Water Vapor Transport and Rainfall, Geophys. Res. Lett., 48, e2020GL090999, https://doi.org/10.1029/2020gl090999, 2021. a
Munday, C., Engelstaedter, S., Ouma, G., Ogutu, G., Olago, D., Ong’ech, D., Lees, T., Wanguba, B., Nkatha, R., Ogolla, C., Gàlgalo, R. A., Dokata, A. J., Kirui, E., Hope, R., and Washington, R.: Observations of the Turkana Jet and the East African Dry Tropics: The RIFTJet Field Campaign, B. Am. Meteor. Soc., 103, E1828–E1842, https://doi.org/10.1175/bams-d-21-0214.1, 2022. a
Myhre, G., Lund Myhre, C., Samset, B. H., and Storelvmo, T.: Aerosols and their Relation to Global Climate and Climate Sensitivity, Nature Education Knowledge, 4, https://www.nature.com/scitable/knowledge/library/aerosols-and-their-relation-to-global-climate-102215345/ (last access: 26 August 2025), 2013a. a
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and natural radiative forcing, Cambridge University Press, Cambridge, UK, 659–740, https://doi.org/10.1017/CBO9781107415324.018, 2013b. a
Narayanasetti, S., Panickal, S., Raghavan, K., Gopinathan, P. A., Choudhury, A. D., and Singh, M.: CCCR-IITM IITM-ESM model output prepared for CMIP6 CMIP piControl, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/AR6.C6CMCIIITpc, 2019. a
NASA GISS: NASA-GISS GISS-E2-2-G model output prepared for CMIP6 CMIP amip, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/AR6.C6CMGIGE2pc, 2019a. a
NASA GISS: NASA-GISS GISS-E2.2H model output prepared for CMIP6 CMIP piControl, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.15871, 2019b. a
Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stoll, J., Folini, D. S., Tegen, I., Wieners, K.-H., Mauritsen, T., Stemmler, I., Barthel, S., Bey, I., Daskalakis, N., Heinold, B., Kokkola, H., Partridge, D., Rast, S., Schmidt, H., Schutgens, N., Stanelle, T., Stier, P., Watson-Parris, D., and Lohmann, U.: HAMMOZ-Consortium MPI-ESM1.2-HAM model output prepared for CMIP6 AerChemMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.1621, 2019. a, b
Niang, C., Mancho, A. M., García-Garrido, V. J., Mohino, E., Rodriguez-Fonseca, B., and Curbelo, J.: Transport pathways across the West African Monsoon as revealed by Lagrangian Coherent Structures, Sci. Rep., 10, 12543, https://doi.org/10.1038/s41598-020-69159-9, 2020. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a, b
Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., Lee, J.-L., Lee, H. J., Hyun, S.-H., Lee, K.-Y., Lee, J.-H., Park, Y.-G., Jin, H., Park, H., and Kim, Y. H.: Korea Institute of Ocean Science and Technology Earth System Model and Its Simulation Characteristics, Ocean Sci. J., 56, 18–45, https://doi.org/10.1007/s12601-021-00001-7, 2021. a
Pan, X., Chin, M., Gautam, R., Bian, H., Kim, D., Colarco, P. R., Diehl, T. L., Takemura, T., Pozzoli, L., Tsigaridis, K., Bauer, S., and Bellouin, N.: A multi-model evaluation of aerosols over South Asia: common problems and possible causes, Atmos. Chem. Phys., 15, 5903–5928, https://doi.org/10.5194/acp-15-5903-2015, 2015. a
Peano, D., Lovato, T., and Materia, S.: CMCC CMCC-ESM2 model output prepared for CMIP6 LS3MIP, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/AR6.C6CMCMCMChi, 2020. a, b
Persad, G. G.: The dependence of aerosols' global and local precipitation impacts on the emitting region, Atmos. Chem. Phys., 23, 3435–3452, https://doi.org/10.5194/acp-23-3435-2023, 2023. a
Peterson, P., Funk, C., Landsfeld, M., Husak, G., Pedreros, D., Verdin, J., Rowland, J., Shukla, S., McNally, A., Michaelsen, J., and Verdin, A.: The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) Dataset: Quasi-Global Precipitation Estimates for Drought Monitoring and Trend Analysis, https://agu.confex.com/agu/fm14/meetingapp.cgi/Paper/8104 (last access: 21 August 2025), 2014. a
Porkka, M., Wang-Erlandsson, L., Destouni, G., Ekman, A. M. L., Rockström, J., and Gordon, L. J.: Is wetter better? Exploring agriculturally-relevant rainfall characteristics over four decades in the Sahel, Environ. Res. Lett., 16, 035002, https://doi.org/10.1088/1748-9326/abdd57, 2021. a
Raheja, G., Nimo, J., Appoh, E. K.-E., Essien, B., Sunu, M., Nyante, J., Amegah, M., Quansah, R., Arku, R. E., Penn, S. L., Giordano, M. R., Zheng, Z., Jack, D., Chillrud, S., Amegah, K., Subramanian, R., Pinder, R., Appah-Sampong, E., Tetteh, E. N., Borketey, M. A., Hughes, A. F., and Westervelt, D. M.: Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana, Enviro. Sci. Technol., 57, 10708–10720, https://doi.org/10.1021/acs.est.2c09264, 2023. a
Ramanathan, V. and Carmichael, G.: Global and regional climate changes due to black carbon, Nat. Geosci., 1, 221–227, 2008. a
Reddington, C. L., Spracklen, D. V., Artaxo, P., Ridley, D. A., Rizzo, L. V., and Arana, A.: Analysis of particulate emissions from tropical biomass burning using a global aerosol model and long-term surface observations, Atmos. Chem. Phys., 16, 11083–11106, https://doi.org/10.5194/acp-16-11083-2016, 2016. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6109, 2019a. a
Ridley, J., Menary, M., Kuhlbrodt, T., Andrews, M., and Andrews, T.: MOHC HadGEM3-GC31-MM model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6112, 2019b. a
Rind, D., Orbe, C., Jonas, J., Nazarenko, L., Zhou, T., Kelley, M., Lacis, A., Shindell, D., Faluvegi, G., Romanou, A., Russell, G., Tausnev, N., Bauer, M., and Schmidt, G.: GISS model E2.2: A climate model optimized for the middle atmosphere – model structure, climatology, variability, and climate sensitivity, J. Geophys. Res., 125, e2019JD032204, https://doi.org/10.1029/2019JD032204, 2020. a, b
Rong, X.: CAMS CAMS_CSM1.0 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.9754, 2019. a
Rong, X., Li, J., Chen, H., Xin, Y., Su, J., Hua, L., Zhou, T., Qi, Y., Zhang, Z., Zhang, G., and Li, J.: The CAMS Climate System Model and a Basic Evaluation of Its Climatology and Climate Variability Simulation, J. Meteorol. Res., 32, 839–861, https://doi.org/10.1007/s13351-018-8058-x, 2018. a
Samset, B. H.: Aerosol absorption has an underappreciated role in historical precipitation change, Commun. Earth Environ., 3, 242, https://doi.org/10.1038/s43247-022-00576-6, 2022. a
Samset, B. H., Sand, M., Smith, C. J., Bauer, S. E., Forster, P. M., Fuglestvedt, J. S., Osprey, S., and Schleussner, C.: Climate Impacts From a Removal of Anthropogenic Aerosol Emissions, Geophys. Res. Lett., 45, 1020–1029, https://doi.org/10.1002/2017gl076079, 2018. a
Scannell, C., Booth, B. B. B., Dunstone, N. J., Rowell, D. P., Bernie, D. J., Kasoar, M., Voulgarakis, A., Wilcox, L. J., Acosta Navarro, J. C., Seland, Y., and Paynter, D. J.: The Influence of Remote Aerosol Forcing from Industrialized Economies on the Future Evolution of East and West African Rainfall, J. Climate, 32, 8335–8354, https://doi.org/10.1175/jcli-d-18-0716.1, 2019. a, b
Schutgens, N. A. J., Gryspeerdt, E., Weigum, N., Tsyro, S., Goto, D., Schulz, M., and Stier, P.: Will a perfect model agree with perfect observations? The impact of spatial sampling, Atmos. Chem. Phys., 16, 6335–6353, https://doi.org/10.5194/acp-16-6335-2016, 2016. a
Schwarzwald, K., Goddard, L., Seager, R., Ting, M., and Marvel, K.: Understanding CMIP6 biases in the representation of the Greater Horn of Africa long and short rains, Clim. Dynam., 61, 1229–1255, https://doi.org/10.1007/s00382-022-06622-5, 2022. a, b
Schwarzwald, K., Goddard, L., Seager, R., Ting, M., and Marvel, K.: Understanding CMIP6 biases in the representation of the greater Horn of Africa long and short rains, Clim. Dynam., 61, 1229–1255, https://doi.org/10.1007/s00382-022-06622-5, 2023. a
Scoccimarro, E., Bellucci, A., and Peano, D.: CMCC CMCC-CM2-HR4 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3823, 2020. a
Seferian, R.: CNRM-CERFACS CNRM-ESM2-1 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4068, 2018. a
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., Sénési, S., Franchisteguy, L., Vial, J., Mallet, M., Joetzjer, E., Geoffroy, O., Guérémy, J.-F., Moine, M.-P., Msadek, R., Ribes, A., Rocher, M., Roehrig, R., Salas-y Mélia, D., Sanchez, E., Terray, L., Valcke, S., Waldman, R., Aumont, O., Bopp, L., Deshayes, J., Éthé, C., and Madec, G.: Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth System Processes in Present-Day and Future Climate, J. Adv. Model. Earth Sy., 11, 4182–4227, https://doi.org/10.1029/2019MS001791, 2019. a
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics, Wiley-Blackwell, Hoboken, NJ, 3rd Edn., ISBN 978-1-118-94740-1, 2016. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020. a, b
Seland, y., Bentsen, M., Oliviè, D. J. L., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O. A., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: NCC NorESM2-LM model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.8036, 2019. a
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire, A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S., de Mora, L., Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R., Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T., Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi, M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A. B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D., Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V., Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T., Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K. Earth System Model, J. Adv. Model. Earth Sy., 11, 4513–4558, https://doi.org/10.1029/2019MS001739, 2019. a
Semmler, T., Danilov, S., Rackow, T., Sidorenko, D., Barbi, D., Hegewald, J., Sein, D., Wang, Q., and Jung, T.: AWI AWI-CM1.1MR model output prepared for CMIP6 CMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.359, 2018. a
Semmler, T., Danilov, S., Gierz, P., Goessling, H. F., Hegewald, J., Hinrichs, C., Koldunov, N., Khosravi, N., Mu, L., Rackow, T., Sein, D. V., Sidorenko, D., Wang, Q., and Jung, T.: Simulations for CMIP6 With the AWI Climate Model AWI‐CM‐1‐1, J. Adv. Model. Earth Sy., 12, e2019MS002009, https://doi.org/10.1029/2019ms002009, 2020. a
Senghor, H., Machu, É., Hourdin, F., and Gaye, A. T.: Seasonal cycle of desert aerosols in western Africa: analysis of the coastal transition with passive and active sensors, Atmos. Chem. Phys., 17, 8395–8410, https://doi.org/10.5194/acp-17-8395-2017, 2017. a
Sepulchre, P., Caubel, A., Ladant, J.-B., Bopp, L., Boucher, O., Braconnot, P., Brockmann, P., Cozic, A., Donnadieu, Y., Dufresne, J.-L., Estella-Perez, V., Ethé, C., Fluteau, F., Foujols, M.-A., Gastineau, G., Ghattas, J., Hauglustaine, D., Hourdin, F., Kageyama, M., Khodri, M., Marti, O., Meurdesoif, Y., Mignot, J., Sarr, A.-C., Servonnat, J., Swingedouw, D., Szopa, S., and Tardif, D.: IPSL-CM5A2 – an Earth system model designed for multi-millennial climate simulations, Geosci. Model Dev., 13, 3011–3053, https://doi.org/10.5194/gmd-13-3011-2020, 2020. a
Shindell, D., Faluvegi, G., Parsons, L., Nagamoto, E., and J., C.: Premature deaths in Africa due to particulate matter under high and low warming scenarios, GeoHealth, 6, e2022GH000 601, https://doi.org/10.1029/2022GH000601, 2022. a
Shindell, D., Parsons, L., Faluvegi, G., Hicks, K., Kuylenstierna, J., and Heaps, C.: The important role of African emissions reductions in projected local rainfall changes, Npj Clim. Atmos. Sci., 6, 47, https://doi.org/10.1038/s41612-023-00382-7, 2023. a
Shindell, D. T., Voulgarakis, A., Faluvegi, G., and Milly, G.: Precipitation response to regional radiative forcing, Atmos. Chem. Phys., 12, 6969–6982, https://doi.org/10.5194/acp-12-6969-2012, 2012. a
Shonk, J. K. P., Turner, A. G., Chevuturi, A., Wilcox, L. J., Dittus, A. J., and Hawkins, E.: Uncertainty in aerosol radiative forcing impacts the simulated global monsoon in the 20th century, Atmos. Chem. Phys., 20, 14903–14915, https://doi.org/10.5194/acp-20-14903-2020, 2020. a
Sidorenko, D., Rackow, T., Jung, T., Semmler, T., Barbi, D., Danilov, S., Dethloff, K., Dorn, W., Fieg, K., Goessling, H. F., Handorf, D., Harig, S., Hiller, W., Juricke, S., Losch, M., Schröter, J., Sein, D. V., and Wang, Q.: Towards multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model formulation and mean climate, Clim. Dynam., 44, 757–780, https://doi.org/10.1007/s00382-014-2290-6, 2015. a
Stier, P., van den Heever, S. C., Christensen, M. W., Gryspeerdt, E., Dagan, G., Saleeby, S. M., Bollasina, M., Donner, L., Emanuel, K., Ekman, A. M. L., Feingold, G., Field, P., Forster, P., Haywood, J., Kahn, R., Koren, I., Kummerow, C., L’Ecuyer, T., Lohmann, U., Ming, Y., Myhre, G., Quaas, J., Rosenfeld, D., Samset, B., Seifert, A., Stephens, G., and Tao, W.-K.: Multifaceted aerosol effects on precipitation, Nat. Geosci., 17, 719–732, https://doi.org/10.1038/s41561-024-01482-6, 2024. a
Stouffer, R.: UA MCM-UA-1-0 model output prepared for CMIP6 ScenarioMIP ssp370, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/AR6.C6SPUAMU, 2019. a, b
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3610, 2019a. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., Sigmond, M., Abraham, C., Akingunola, A., and Reader, C.: CCCma CanESM5.1 model output prepared for CMIP6 CMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.17184, 2019b. a, b
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L., von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5-CanOE model output prepared for CMIP6 CMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.10205, 2019c. 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, 2019d. a
Sylla, M. B., Giorgi, F., Coppola, E., and Mariotti, L.: Uncertainties in daily rainfall over Africa: assessment of gridded observation products and evaluation of a regional climate model simulation, Int. J. Climatol., 33, 1805–1817, 2013. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton, J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6113, 2019. a
Tatebe, H. and Watanabe, M.: MIROC MIROC6 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5603, 2018. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019. a
Thornhill, G. D., Collins, W. J., Kramer, R. J., Olivié, D., Skeie, R. B., O'Connor, F. M., Abraham, N. L., Checa-Garcia, R., Bauer, S. E., Deushi, M., Emmons, L. K., Forster, P. M., Horowitz, L. W., Johnson, B., Keeble, J., Lamarque, J.-F., Michou, M., Mills, M. J., Mulcahy, J. P., Myhre, G., Nabat, P., Naik, V., Oshima, N., Schulz, M., Smith, C. J., Takemura, T., Tilmes, S., Wu, T., Zeng, G., and Zhang, J.: Effective radiative forcing from emissions of reactive gases and aerosols – a multi-model comparison, Atmos. Chem. Phys., 21, 853–874, https://doi.org/10.5194/acp-21-853-2021, 2021. a
Tramblay, Y. and Villarini, G.: Flood trends in Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7104, https://doi.org/10.5194/egusphere-egu2020-7104, 2020 a
Turnock, S. T., Allen, R. J., Andrews, M., Bauer, S. E., Deushi, M., Emmons, L., Good, P., Horowitz, L., John, J. G., Michou, M., Nabat, P., Naik, V., Neubauer, D., O'Connor, F. M., Olivié, D., Oshima, N., Schulz, M., Sellar, A., Shim, S., Takemura, T., Tilmes, S., Tsigaridis, K., Wu, T., and Zhang, J.: Historical and future changes in air pollutants from CMIP6 models, Atmos. Chem. Phys., 20, 14547–14579, https://doi.org/10.5194/acp-20-14547-2020, 2020. a, b, c, d, e, f, g, h
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:tiopot>2.0.co;2, 1977. a
Twomey, S. A., Piepgrass, M., and Wolfe, T. L.: An assessment of the impact of pollution on global cloud albedo, Tellus B, 36B, 356–366, https://doi.org/10.1111/j.1600-0889.1984.tb00254.x, 1984. a
U. S. Environmental Protection Agency: Reference and equivalent method applications, https://www.epa.gov/sites/default/files/2017-02/documents/frmfemguidelines.pdf (last access: 6 August 2024), 2011. a
United Nations Environment Programme: Global environment outlook 6 (GEO-6), United Nations Environment Programme, Genève, Switzerland, https://doi.org/10.1017/9781108627146, 2017. a
US EPA: CMAQ, Zenodo [code], https://doi.org/10.5281/ZENODO.1079878, 2024. a
van Noije, T., Bergman, T., Le Sager, P., O'Donnell, D., Makkonen, R., Gonçalves-Ageitos, M., Döscher, R., Fladrich, U., von Hardenberg, J., Keskinen, J.-P., Korhonen, H., Laakso, A., Myriokefalitakis, S., Ollinaho, P., Pérez García-Pando, C., Reerink, T., Schrödner, R., Wyser, K., and Yang, S.: EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6 , Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, 2021. a
Vogel, A., Alessa, G., Scheele, R., Weber, L., Dubovik, O., North, P., and Fiedler, S.: Uncertainty in Aerosol Optical Depth From Modern Aerosol‐Climate Models, Reanalyses, and Satellite Products, J. Geophys. Res.-Atmos., 127, e2021JD035483, https://doi.org/10.1029/2021jd035483, 2022. a, b
Voldoire, A.: CMIP6 simulations of the CNRM-CERFACS based on CNRM-CM6-1 model for CMIP experiment historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4066, 2018. a
Voldoire, A.: CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp585, World Data Center for Climate (WDCC) at DKRZ [data set], https://doi.org/10.26050/WDCC/AR6.C6SPCECC2, 2019. a
Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J.-F., Michou, M., Moine, M.-P., Nabat, P., Roehrig, R., Salas y Mélia, D., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., Cattiaux, J., Deshayes, J., Douville, H., Ethé, C., Franchistéguy, L., Geoffroy, O., Lévy, C., Madec, G., Meurdesoif, Y., Msadek, R., Ribes, A., Sanchez-Gomez, E., Terray, L., and Waldman, R.: Evaluation of CMIP6 DECK Experiments With CNRM-CM6-1, J. Adv. Model. Earth Sy., 11, 2177–2213, https://doi.org/10.1029/2019MS001683, 2019. a, b
Volodin, E., Mortikov, E., Gritsun, A., Lykossov, V., Galin, V., Diansky, N., Gusev, A., Kostrykin, S., Iakovlev, N., Shestakova, A., and Emelina, S.: INM INM-CM4-8 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5069, 2019a. a
Volodin, E., Mortikov, E., Gritsun, A., Lykossov, V., Galin, V., Diansky, N., Gusev, A., Kostrykin, S., Iakovlev, N., Shestakova, A., and Emelina, S.: INM INM-CM5-0 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.5070, 2019b. a
Volodin, E. M. and Kostrykin, S. V.: The aerosol module in the INM RAS climate model, Russ. Meteoro. Hydro., 41, 519–528, https://doi.org/10.3103/S106837391608001X, 2016. a
Volodin, E. M., Dianskii, N. A., and Gusev, A. V.: Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations, Izvestiya, Atmos. Ocean. Phys., 46, 414–431, https://doi.org/10.1134/S000143381004002X, 2010. a
Volodin, E. M., Mortikov, E. V., Kostrykin, S. V., Galin, V. Y., Lykossov, V. N., Gritsun, A. S., Diansky, N. A., Gusev, A. V., and Iakovlev, N. G.: Simulation of the present-day climate with the climate model INMCM5, Clim. Dynam., 49, 3715–3734, https://doi.org/10.1007/s00382-017-3539-7, 2017. a
Wagner, S. C., Govaerts, Y. M., and Lattanzio, A.: Joint retrieval of surface reflectance and aerosol optical depth from MSG/SEVIRI observations with an optimal estimation approach: 2. Implementation and evaluation, J. Geophys. Res., 115, D02204, https://doi.org/10.1029/2009JD011780, 2010. a
Walton, J., Mulcahy, J., Tang, Y., Rumbold, S., Hardacre, C., Stringer, M., Hill, R., Kuhlbrodt, T., and Jones, C.: MOHC UKESM1.1-LL model output prepared for CMIP6 ScenarioMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.16841, 2022. a
Wang, R., Cui, K., Sheu, H.-L., Wang, L.-C., and Liu, X.: Effects of Precipitation on the Air Quality Index, PM2.5 Levels and on the Dry Deposition of PCDD/Fs in the Ambient Air, Aerosol Air Qual. Res., 23, 220 417, https://doi.org/10.4209/aaqr.220417, 2023. a, b
Wang, Y.-C., Hsu, H.-H., Chen, C.-A., Tseng, W.-L., Hsu, P.-C., Lin, C.-W., Chen, Y.-L., Jiang, L.-C., Lee, Y.-C., Liang, H.-C., Chang, W.-M., Lee, W.-L., and Shiu, C.-J.: Performance of the Taiwan Earth System Model in simulating climate variability compared with observations and CMIP6 model simulations, J. Adv. Model. Earth Sy., 13, e2020MS002353, https://doi.org/10.1029/2020MS002353, 2021. a
Watanabe, S., Hajima, T., Sudo, K., Abe, M., Arakawa, O., Ogochi, K., Arakawa, T., Tatebe, H., Ito, A., Ito, A., Komuro, Y., Nitta, T., Noguchi, M. A., Ogura, T., Ohgaito, R., Sekiguchi, M., Suzuki, T., Tachiiri, K., Takata, K., Takemura, T., Watanabe, M., Yamamoto, A., Yamazaki, D., Yoshimura, K., and Kawamiya, M.: MIROC MIROC-ES2H model output prepared for CMIP6 GeoMIP, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.907, 2021. a
Wei, G., Sun, P., Jiang, S., Shen, Y., Liu, B., Zhang, Z., and Ouyang, X.: The Driving Influence of Multi-Dimensional Urbanization on PM2.5 Concentrations in Africa: New Evidence from Multi-Source Remote Sensing Data, 2000–2018, Int. J. Environ. Res. Publ. Hlth., 18, 9389, https://doi.org/10.3390/ijerph18179389, 2021. a
Wells, C. D., Kasoar, M., Bellouin, N., and Voulgarakis, A.: Local and remote climate impacts of future African aerosol emissions, Atmos. Chem. Phys., 23, 3575–3593, https://doi.org/10.5194/acp-23-3575-2023, 2023. a
Westervelt, D. M., Conley, A. J., Fiore, A. M., Lamarque, J., Shindell, D., Previdi, M., Faluvegi, G., Correa, G., and Horowitz, L. W.: Multimodel precipitation responses to removal of U.S. sulfur dioxide emissions, J. Geophys. Res.-Atmos., 122, 5024–5038, https://doi.org/10.1002/2017jd026756, 2017. a
Westervelt, D. M., Mascioli, N. R., Fiore, A. M., Conley, A. J., Lamarque, J.-F., Shindell, D. T., Faluvegi, G., Previdi, M., Correa, G., and Horowitz, L. W.: Local and remote mean and extreme temperature response to regional aerosol emissions reductions, Atmos. Chem. Phys., 20, 3009–3027, https://doi.org/10.5194/acp-20-3009-2020, 2020. a
Westervelt, D. M., Isevulambire, P. K., Yombo Phaka, R., Yang, L. H., Raheja, G., Milly, G., Selenge, J.-L. B., Mulumba, J. P. M., Bousiotis, D., Djibi, B. L., McNeill, V. F., Ng, N. L., Pope, F., Mbela, G. K., and Konde, J. N.: Low-Cost Investigation into Sources of PM2.5 in Kinshasa, Democratic Republic of the Congo, ACS ES& T Air, 1, 43–51, https://doi.org/10.1021/acsestair.3c00024, 2023. a
Wilcox, L. J., Allen, R. J., Samset, B. H., Bollasina, M. A., Griffiths, P. T., Keeble, J., Lund, M. T., Makkonen, R., Merikanto, J., O'Donnell, D., Paynter, D. J., Persad, G. G., Rumbold, S. T., Takemura, T., Tsigaridis, K., Undorf, S., and Westervelt, D. M.: The Regional Aerosol Model Intercomparison Project (RAMIP), Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, 2023. a
Williams, A. I. L., Stier, P., Dagan, G., and Watson-Parris, D.: Strong control of effective radiative forcing by the spatial pattern of absorbing aerosol, Nat. Clim. Change, 12, 735–742, https://doi.org/10.1038/s41558-022-01415-4, 2022. a
Williams, A. I. L., Watson-Parris, D., Dagan, G., and Stier, P.: Dependence of Fast Changes in Global and Local Precipitation on the Geographical Location of Absorbing Aerosol, J. Climate, 36, 6163–6176, https://doi.org/10.1175/jcli-d-23-0022.1, 2023. a
Woodward, S., Sellar, A. A., Tang, Y., Stringer, M., Yool, A., Robertson, E., and Wiltshire, A.: The simulation of mineral dust in the United Kingdom Earth System Model UKESM1, Atmos. Chem. Phys., 22, 14503–14528, https://doi.org/10.5194/acp-22-14503-2022, 2022. a
Wu, C., Lin, Z., Liu, X., Ji, D., Zhang, H., Li, C., and Lin, G.: Description of Dust Emission Parameterization in CAS‐ESM2 and Its Simulation of Global Dust Cycle and East Asian Dust Events, J. Adv. Model. Earth Sy., 13, e2020MS002456, https://doi.org/10.1029/2020ms002456, 2021. a
Wu, T., Chu, M., Dong, M., Fang, Y., Jie, W., Li, J., Li, W., Liu, Q., Shi, X., Xin, X., Yan, J., Zhang, F., Zhang, J., Zhang, L., and Zhang, Y.: BCC BCC-CSM2MR model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2948, 2018. a
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M., Huang, A., Zhang, Y., and Liu, X.: The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6 , Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019. a
Wu, T., Zhang, F., Zhang, J., Jie, W., Zhang, Y., Wu, F., Li, L., Yan, J., Liu, X., Lu, X., Tan, H., Zhang, L., Wang, J., and Hu, A.: Beijing Climate Center Earth System Model version 1 (BCC-ESM1): model description and evaluation of aerosol simulations, Geosci. Model Dev., 13, 977–1005, https://doi.org/10.5194/gmd-13-977-2020, 2020. a
Xian, P., Klotzbach, P. J., Dunion, J. P., Janiga, M. A., Reid, J. S., Colarco, P. R., and Kipling, Z.: Revisiting the relationship between Atlantic dust and tropical cyclone activity using aerosol optical depth reanalyses: 2003–2018, Atmos. Chem. Phys., 20, 15357–15378, https://doi.org/10.5194/acp-20-15357-2020, 2020. a
YU, Y.: CAS FGOALS-f3-L model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.3355, 2019. a
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component, J. Meteor. Soc. Jpn. Ser. II, 97, 931–965, https://doi.org/10.2151/jmsj.2019-051, 2019a. a
Yukimoto, S., Koshiro, T., Kawai, H., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yoshimura, H., Shindo, E., Mizuta, R., Ishii, M., Obata, A., and Adachi, Y.: MRI MRI-ESM2.0 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.6842, 2019b. a
Zelinka, M. D., Smith, C. J., Qin, Y., and Taylor, K. E.: Comparison of methods to estimate aerosol effective radiative forcings in climate models, Atmos. Chem. Phys., 23, 8879–8898, https://doi.org/10.5194/acp-23-8879-2023, 2023. a
Zhang, H., Zhang, M., Jin, J., Fei, K., Ji, D., Wu, C., Zhu, J., He, J., Chai, Z., Xie, J., Dong, X., Zhang, D., Bi, X., Cao, H., Chen, H., Chen, K., Chen, X., Gao, X., Hao, H., Jiang, J., Kong, X., Li, S., Li, Y., Lin, P., Lin, Z., Liu, H., Liu, X., Shi, Y., Song, M., Wang, H., Wang, T., Wang, X., Wang, Z., Wei, Y., Wu, B., Xie, Z., Xu, Y., Yu, Y., Yuan, L., Zeng, Q., Zeng, X., Zhao, S., Zhou, G., and Zhu, J.: Description and Climate Simulation Performance of CAS-ESM Version 2, J. Adv. Model. Earth Sy., 12, e2020MS002210, https://doi.org/10.1029/2020MS002210, 2020. a, b
Zhang, J., Wu, T., Shi, X., Zhang, F., Li, J., Chu, M., Liu, Q., Yan, J., Ma, Q., and Wei, M.: BCC BCC-ESM1 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.2949, 2018. a
Ziehn, T., Chamberlain, M., Lenton, A., Law, R., Bodman, R., Dix, M., Wang, Y., Dobrohotoff, P., Srbinovsky, J., Stevens, L., Vohralik, P., Mackallah, C., Sullivan, A., O'Farrell, S., and Druken, K.: CSIRO ACCESS-ESM1.5 model output prepared for CMIP6 CMIP historical, Earth System Grid Federation [data set], https://doi.org/10.22033/ESGF/CMIP6.4272, 2019. a
Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., Stevens, L., Wang, Y.-P., and Srbinovsky, J.: The Australian Earth System Model: ACCESS-ESM1.5, Journal of Southern Hemisphere Earth Systems Science, 70, 193–214, https://doi.org/10.1071/ES19035, 2020. a
Short summary
Our research explores how well air pollution and rainfall patterns in Africa are represented in current climate models by comparing model data to observations from 1981 to 2023. While most models capture seasonal air quality changes well, they struggle to replicate the distribution of non-dust pollutants and certain rainfall patterns, especially over east Africa. Improving these models is crucial for better climate predictions and preparing for future risks.
Our research explores how well air pollution and rainfall patterns in Africa are represented in...
Altmetrics
Final-revised paper
Preprint