Articles | Volume 23, issue 24
https://doi.org/10.5194/acp-23-15181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-15181-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Dec 2023
Research article |
| 14 Dec 2023
| 14 Dec 2023
The sensitivity of Southern Ocean atmospheric dimethyl sulfide (DMS) to modeled oceanic DMS concentrations and emissions
Yusuf A. Bhatti
CORRESPONDING AUTHOR
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa / New Zealand
Laura E. Revell
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa / New Zealand
Alex J. Schuddeboom
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa / New Zealand
now at: National Institute of Water and Atmospheric Research (NIWA), Christchurch, Aotearoa / New Zealand
Adrian J. McDonald
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa / New Zealand
Gateway Antarctica, University of Canterbury, Christchurch, Aotearoa / New Zealand
Alex T. Archibald
National Centre for Atmospheric Science, Cambridge, United Kingdom
Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
Jonny Williams
National Institute of Water and Atmospheric Research (NIWA), Wellington, Aotearoa / New Zealand
Abhijith U. Venugopal
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa / New Zealand
Catherine Hardacre
Met Office, Exeter, EX1 3PB, United Kingdom
now at: School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa / New Zealand
Erik Behrens
National Institute of Water and Atmospheric Research (NIWA), Wellington, Aotearoa / New Zealand
Related authors
Yusuf Bhatti, Duncan Watson-Parris, Leighton Regayre, Hailing Jia, David Neubauer, Ulas Im, Carl Svenhag, Nick Schutgens, Athanasios Tsikerdekis, Athanasios Nenes, Irfan Muhammed, Bastiaan van Diedenhoven, Ardit Arifi, Guangliang Fu, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2025-2848, https://doi.org/10.5194/egusphere-2025-2848, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Aerosols (small airborne particles) impact Earth's climate, but their extent is unknown. By running climate model simulations and emulating millions of additional variants with different settings, we found that natural emissions like sea spray and sulfur are key sources of uncertainty in climate predictions. Our work shows that understanding these natural processes better can help improve climate models and make future climate projections more accurate.
Gunnar Myhre, Øivind Hodnebrog, Srinath Krishnan, Maria Sand, Marit Sandstad, Ragnhild B. Skeie, Lieven Clarisse, Bruno Franco, Dylan B. Millet, Kelley C. Wells, Alexander Archibald, Hannah N. Bryant, Alex T. Chaudhri, David S. Stevenson, Didier Hauglustaine, Michael Prather, J. Christopher Kaiser, Dirk J. L. Olivie, Michael Schulz, Oliver Wild, Ye Wang, Thérèse Salameh, Jason E. Williams, Philippe Le Sager, Fabien Paulot, Kostas Tsigaridis, and Haley E. Plaas
EGUsphere, https://doi.org/10.5194/egusphere-2025-3057, https://doi.org/10.5194/egusphere-2025-3057, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Volatile organic compounds (VOCs) affect air quality and climate, but their behavior in the atmosphere is still uncertain. We launched a global research effort to compare how different models represent these compounds and to improve their accuracy. By analyzing model results alongside observations and satellite data, we aim to better understand the atmospheric composition of these compounds.
Xu-Cheng He, Nathan Luke Abraham, Han Ding, Maria R. Russo, Daniel P. Grosvenor, Yao Ge, Xuemei Wang, Anthony C. Jones, Pedro Campuzano-Jost, Benjamin Nault, Agnieszka Kupc, Donald Blake, Jose L. Jimenez, Christina J. Williamson, Kenneth S. Carslaw, James Weber, Alexander T. Archibald, and Hamish Gordon
EGUsphere, https://doi.org/10.5194/egusphere-2025-3700, https://doi.org/10.5194/egusphere-2025-3700, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Aerosols affect clouds and climate. However, current climate models still struggle to simulate them accurately. We used aircraft data from a global mission to evaluate how well the UK Earth System Model represents aerosols and their precursors. Our results show that the model misses key formation processes in clean ocean regions, suggesting that future improvements should focus on better representing how aerosols form naturally in the atmosphere.
Christian Lewis, Rachel Corran, Sara E. Mikaloff-Fletcher, Erik Behrens, Rowena Moss, Gordon Brailsford, Andrew Lorrey, Margaret Norris, and Jocelyn Turnbull
Biogeosciences, 22, 4187–4201, https://doi.org/10.5194/bg-22-4187-2025, https://doi.org/10.5194/bg-22-4187-2025, 2025
Short summary
Short summary
The Southern Ocean carbon sink is a balance between two opposing forces: CO2 absorption at mid-latitudes and CO2 outgassing at high latitudes. Radiocarbon analysis can be used to constrain the latter, as upwelling waters outgas old CO2, diluting atmospheric radiocarbon content. We present tree-ring radiocarbon measurements from Aotearoa / New Zealand and Chile. We show that low radiocarbon in Aotearoa / New Zealand’s Motu Ihupuku / Campbell Island is linked to outgassing in the critical Antarctic Southern Zone.
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.
Megan A. J. Brown, Nicola J . Warwick, Nathan Luke Abraham, Paul T. Griffiths, Steve T. Rumbold, Gerd A. Folberth, Fiona M. O'Connor, and Alex T. Archibald
EGUsphere, https://doi.org/10.5194/egusphere-2025-2676, https://doi.org/10.5194/egusphere-2025-2676, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Hydrogen (H2) is an indirect greenhouse gas by increasing methane (CH4) lifetime. Interaction between H2 and CH4 is important for hydrogen’s global warming potential (GWP). Global models do not represent this interaction well; H2 or CH4 are prescribed at the surface. We implement an interactive H2 scheme into a global model coupled with interactive CH4. We simulate scenarios demonstrating its capability, improving model performance and more accurately representing H2-CH4 interaction.
Yusuf Bhatti, Duncan Watson-Parris, Leighton Regayre, Hailing Jia, David Neubauer, Ulas Im, Carl Svenhag, Nick Schutgens, Athanasios Tsikerdekis, Athanasios Nenes, Irfan Muhammed, Bastiaan van Diedenhoven, Ardit Arifi, Guangliang Fu, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2025-2848, https://doi.org/10.5194/egusphere-2025-2848, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Aerosols (small airborne particles) impact Earth's climate, but their extent is unknown. By running climate model simulations and emulating millions of additional variants with different settings, we found that natural emissions like sea spray and sulfur are key sources of uncertainty in climate predictions. Our work shows that understanding these natural processes better can help improve climate models and make future climate projections more accurate.
Martin Richard Willett, Melissa Brooks, Andrew Bushell, Paul Earnshaw, Samantha Smith, Lorenzo Tomassini, Martin Best, Ian Boutle, Jennifer Brooke, John M. Edwards, Kalli Furtado, Catherine Hardacre, Andrew J. Hartley, Alan Hewitt, Ben Johnson, Adrian Lock, Andy Malcolm, Jane Mulcahy, Eike Müller, Heather Rumbold, Gabriel G. Rooney, Alistair Sellar, Masashi Ujiie, Annelize van Niekerk, Andy Wiltshire, and Michael Whitall
EGUsphere, https://doi.org/10.5194/egusphere-2025-1829, https://doi.org/10.5194/egusphere-2025-1829, 2025
Short summary
Short summary
Global Atmosphere (GA) configurations of the Unified Model (UM) and Global Land (GL) configurations of JULES are developed for use in any global atmospheric modelling application. We describe a recent iteration of these configurations, GA8GL9, which includes improvements to the represenation of convection and other physical processes. GA8GL9 is used for operational weather prediction in the UK and forms the basis for the next GA and GL configuration.
Cameron McErlich, Felix Goddard, Alex Aves, Catherine Hardacre, Nikolaos Evangeliou, and Laura E. Revell
EGUsphere, https://doi.org/10.5194/egusphere-2025-1575, https://doi.org/10.5194/egusphere-2025-1575, 2025
Short summary
Short summary
Airborne microplastics are a new air pollutant but are not yet included in most global models. We add them to the UK Earth System Model to show how they move, change, and are removed from air. Smaller microplastics persist for longer and can travel further, even to Antarctica. While their current role in air pollution is small, their presence is expected to grow in future. This work offers a framework to assess future impacts of microplastics on air quality and climate.
Alex T. Archibald, Bablu Sinha, Maria R. Russo, Emily Matthews, Freya A. Squires, N. Luke Abraham, Stephane J.-B. Bauguitte, Thomas J. Bannan, Thomas G. Bell, David Berry, Lucy J. Carpenter, Hugh Coe, Andrew Coward, Peter Edwards, Daniel Feltham, Dwayne Heard, Jim Hopkins, James Keeble, Elizabeth C. Kent, Brian A. King, Isobel R. Lawrence, James Lee, Claire R. Macintosh, Alex Megann, Bengamin I. Moat, Katie Read, Chris Reed, Malcolm J. Roberts, Reinhard Schiemann, David Schroeder, Timothy J. Smyth, Loren Temple, Navaneeth Thamban, Lisa Whalley, Simon Williams, Huihui Wu, and Mingxi Yang
Earth Syst. Sci. Data, 17, 135–164, https://doi.org/10.5194/essd-17-135-2025, https://doi.org/10.5194/essd-17-135-2025, 2025
Short summary
Short summary
Here, we present an overview of the data generated as part of the North Atlantic Climate System Integrated Study (ACSIS) programme that are available through dedicated repositories at the Centre for Environmental Data Analysis (CEDA; www.ceda.ac.uk) and the British Oceanographic Data Centre (BODC; bodc.ac.uk). The datasets described here cover the North Atlantic Ocean, the atmosphere above (it including its composition), and Arctic sea ice.
Christopher J. Roach, Joao Marcos A. C. de Souza, Erik Behrens, and Stephen J. Stuart
EGUsphere, https://doi.org/10.5194/egusphere-2024-1962, https://doi.org/10.5194/egusphere-2024-1962, 2024
Short summary
Short summary
We have used a 5 km regional ocean model for New Zealand forced with a coarser resolution global model to project changes in under medium and high emissions scenarios. This is necessary since the global model is unable to resolve the small scale processes on the continental shelf which determine climate change may influence fisheries and aquaculture. We see the upper ocean warms at similar rates all around New Zealand, but that the deep ocean shows more rapid warming in the west and south.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Lorrie Simone Denise Jacob, Chiara Giorio, and Alexander Thomas Archibald
Atmos. Chem. Phys., 24, 3329–3347, https://doi.org/10.5194/acp-24-3329-2024, https://doi.org/10.5194/acp-24-3329-2024, 2024
Short summary
Short summary
Recent studies on DMS have provided new challenges to our mechanistic understanding. Here we synthesise a number of recent studies to further develop and extend a state-of-the-art mechanism. Our new mechanism is shown to outperform all existing mechanisms when compared over a wide set of conditions. The development of an improved DMS mechanism will help lead the way to better the understanding the climate impacts of DMS emissions in past, present, and future atmospheric conditions.
Dongqi Lin, Jiawei Zhang, Basit Khan, Marwan Katurji, and Laura E. Revell
Geosci. Model Dev., 17, 815–845, https://doi.org/10.5194/gmd-17-815-2024, https://doi.org/10.5194/gmd-17-815-2024, 2024
Short summary
Short summary
GEO4PALM is an open-source tool to generate static input for the Parallelized Large-Eddy Simulation (PALM) model system. Geospatial static input is essential for realistic PALM simulations. However, existing tools fail to generate PALM's geospatial static input for most regions. GEO4PALM is compatible with diverse geospatial data sources and provides access to free data sets. In addition, this paper presents two application examples, which show successful PALM simulations using GEO4PALM.
Ben A. Cala, Scott Archer-Nicholls, James Weber, N. Luke Abraham, Paul T. Griffiths, Lorrie Jacob, Y. Matthew Shin, Laura E. Revell, Matthew Woodhouse, and Alexander T. Archibald
Atmos. Chem. Phys., 23, 14735–14760, https://doi.org/10.5194/acp-23-14735-2023, https://doi.org/10.5194/acp-23-14735-2023, 2023
Short summary
Short summary
Dimethyl sulfide (DMS) is an important trace gas emitted from the ocean recognised as setting the sulfate aerosol background, but its oxidation is complex. As a result representation in chemistry-climate models is greatly simplified. We develop and compare a new mechanism to existing mechanisms via a series of global and box model experiments. Our studies show our updated DMS scheme is a significant improvement but significant variance exists between mechanisms.
Zhangcheng Pei, Sonya L. Fiddes, W. John R. French, Simon P. Alexander, Marc D. Mallet, Peter Kuma, and Adrian McDonald
Atmos. Chem. Phys., 23, 14691–14714, https://doi.org/10.5194/acp-23-14691-2023, https://doi.org/10.5194/acp-23-14691-2023, 2023
Short summary
Short summary
In this paper, we use ground-based observations to evaluate a climate model and a satellite product in simulating surface radiation and investigate how radiation biases are influenced by cloud properties over the Southern Ocean. We find that significant radiation biases exist in both the model and satellite. The cloud fraction and cloud occurrence play an important role in affecting radiation biases. We suggest further development for the model and satellite using ground-based observations.
Dongqi Lin, Marwan Katurji, Laura E. Revell, Basit Khan, and Andrew Sturman
Atmos. Chem. Phys., 23, 14451–14479, https://doi.org/10.5194/acp-23-14451-2023, https://doi.org/10.5194/acp-23-14451-2023, 2023
Short summary
Short summary
Accurate fog forecasting is difficult in a complex environment. Spatial variations in soil moisture could impact fog. Here, we carried out fog simulations with spatially different soil moisture in complex topography. The soil moisture was calculated using satellite observations. The results show that the spatial variations in soil moisture do not have a significant impact on where fog occurs but do impact how long fog lasts. This finding could improve fog forecasts in the future.
Nicola J. Warwick, Alex T. Archibald, Paul T. Griffiths, James Keeble, Fiona M. O'Connor, John A. Pyle, and Keith P. Shine
Atmos. Chem. Phys., 23, 13451–13467, https://doi.org/10.5194/acp-23-13451-2023, https://doi.org/10.5194/acp-23-13451-2023, 2023
Short summary
Short summary
A chemistry–climate model has been used to explore the atmospheric response to changes in emissions of hydrogen and other species associated with a shift from fossil fuel to hydrogen use. Leakage of hydrogen results in indirect global warming, offsetting greenhouse gas emission reductions from reduced fossil fuel use. To maximise the benefit of hydrogen as an energy source, hydrogen leakage and emissions of methane, carbon monoxide and nitrogen oxides should be minimised.
McKenna W. Stanford, Ann M. Fridlind, Israel Silber, Andrew S. Ackerman, Greg Cesana, Johannes Mülmenstädt, Alain Protat, Simon Alexander, and Adrian McDonald
Atmos. Chem. Phys., 23, 9037–9069, https://doi.org/10.5194/acp-23-9037-2023, https://doi.org/10.5194/acp-23-9037-2023, 2023
Short summary
Short summary
Clouds play an important role in the Earth’s climate system as they modulate the amount of radiation that either reaches the surface or is reflected back to space. This study demonstrates an approach to robustly evaluate surface-based observations against a large-scale model. We find that the large-scale model precipitates too infrequently relative to observations, contrary to literature documentation suggesting otherwise based on satellite measurements.
Jonny Williams, Erik Behrens, Olaf Morgenstern, Peter Gibson, and Joao Teixeira
EGUsphere, https://doi.org/10.5194/egusphere-2023-1694, https://doi.org/10.5194/egusphere-2023-1694, 2023
Preprint withdrawn
Short summary
Short summary
We use open-source cyclone tracking software and state-of-the-art climate models to characterise present-day tropical cyclones – TCs – in the South Pacific before moving on to estimate how they may change in the future. A robust result of this work is the projection of future intensification of TCs. However, the question of their future occurrence frequency is less clear. Under extreme future warming scenarios, we postulate a possible increase in power dissipation per TC of up to 25 %.
Georgia R. Grant, Jonny H. T. Williams, Sebastian Naeher, Osamu Seki, Erin L. McClymont, Molly O. Patterson, Alan M. Haywood, Erik Behrens, Masanobu Yamamoto, and Katelyn Johnson
Clim. Past, 19, 1359–1381, https://doi.org/10.5194/cp-19-1359-2023, https://doi.org/10.5194/cp-19-1359-2023, 2023
Short summary
Short summary
Regional warming will differ from global warming, and climate models perform poorly in the Southern Ocean. We reconstruct sea surface temperatures in the south-west Pacific during the mid-Pliocene, a time 3 million years ago that represents the long-term outcomes of 3 °C warming, which is expected for the future. Comparing these results to climate model simulations, we show that the south-west Pacific region will warm by 1 °C above the global average if atmospheric CO2 remains above 350 ppm.
Maria Rosa Russo, Brian John Kerridge, Nathan Luke Abraham, James Keeble, Barry Graham Latter, Richard Siddans, James Weber, Paul Thomas Griffiths, John Adrian Pyle, and Alexander Thomas Archibald
Atmos. Chem. Phys., 23, 6169–6196, https://doi.org/10.5194/acp-23-6169-2023, https://doi.org/10.5194/acp-23-6169-2023, 2023
Short summary
Short summary
Tropospheric ozone is an important component of the Earth system as it can affect both climate and air quality. In this work we use observed tropospheric ozone derived from satellite observations and compare it to tropospheric ozone from model simulations. Our aim is to investigate recent changes (2005–2018) in tropospheric ozone in the North Atlantic region and to understand what factors are driving such changes.
Scott Archer-Nicholls, Rachel Allen, Nathan L. Abraham, Paul T. Griffiths, and Alex T. Archibald
Atmos. Chem. Phys., 23, 5801–5813, https://doi.org/10.5194/acp-23-5801-2023, https://doi.org/10.5194/acp-23-5801-2023, 2023
Short summary
Short summary
The nitrate radical is a major oxidant at nighttime, but much less is known about it than about the other oxidants ozone and OH. We use Earth system model calculations to show how the nitrate radical has changed in abundance from 1850–2014 and to 2100 under a range of different climate and emission scenarios. Depending on the emissions and climate scenario, significant increases are projected with implications for the oxidation of volatile organic compounds and the formation of fine aerosol.
Ernesto Reyes-Villegas, Douglas Lowe, Jill S. Johnson, Kenneth S. Carslaw, Eoghan Darbyshire, Michael Flynn, James D. Allan, Hugh Coe, Ying Chen, Oliver Wild, Scott Archer-Nicholls, Alex Archibald, Siddhartha Singh, Manish Shrivastava, Rahul A. Zaveri, Vikas Singh, Gufran Beig, Ranjeet Sokhi, and Gordon McFiggans
Atmos. Chem. Phys., 23, 5763–5782, https://doi.org/10.5194/acp-23-5763-2023, https://doi.org/10.5194/acp-23-5763-2023, 2023
Short summary
Short summary
Organic aerosols (OAs), their sources and their processes remain poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem, can be a useful tool to describe primary OA (POA) production and aging. However, the main disadvantage is its complexity. We used a Gaussian process simulator to reproduce model results and to estimate the sources of model uncertainty. We do this by comparing the outputs with OA observations made at Delhi, India, in 2018.
Fouzia Fahrin, Daniel C. Jones, Yan Wu, James Keeble, and Alexander T. Archibald
Atmos. Chem. Phys., 23, 3609–3627, https://doi.org/10.5194/acp-23-3609-2023, https://doi.org/10.5194/acp-23-3609-2023, 2023
Short summary
Short summary
We use a machine learning technique called Gaussian mixture modeling (GMM) to classify vertical ozone profiles into groups based on how the ozone concentration changes with pressure. Even though the GMM algorithm was not provided with spatial information, the classes are geographically coherent. We also detect signatures of tropical broadening in UKESM1 future climate scenarios. GMM may be useful for understanding ozone structures in modeled and observed datasets.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
Short summary
Short summary
Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Peter Kuma, Frida A.-M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland
Atmos. Chem. Phys., 23, 523–549, https://doi.org/10.5194/acp-23-523-2023, https://doi.org/10.5194/acp-23-523-2023, 2023
Short summary
Short summary
We present a machine learning method for determining cloud types in climate model output and satellite observations based on ground observations of cloud genera. We analyse cloud type biases and changes with temperature in climate models and show that the bias is anticorrelated with climate sensitivity. Models simulating decreasing stratiform and increasing cumuliform clouds with increased CO2 concentration tend to have higher climate sensitivity than models simulating the opposite tendencies.
Le Yuan, Olalekan A. M. Popoola, Christina Hood, David Carruthers, Roderic L. Jones, Haitong Zhe Sun, Huan Liu, Qiang Zhang, and Alexander T. Archibald
Atmos. Chem. Phys., 22, 8617–8637, https://doi.org/10.5194/acp-22-8617-2022, https://doi.org/10.5194/acp-22-8617-2022, 2022
Short summary
Short summary
Emission estimates represent a major source of uncertainty in air quality modelling. We developed a novel approach to improve emission estimates from existing inventories using air quality models and routine in situ observations. Using this approach, we derived improved estimates of NOx emissions from the transport sector in Beijing in 2016. This approach has great potential in deriving timely updates of emissions for other pollutants, particularly in regions undergoing rapid emission changes.
Adrien Guyot, Alain Protat, Simon P. Alexander, Andrew R. Klekociuk, Peter Kuma, and Adrian McDonald
Atmos. Meas. Tech., 15, 3663–3681, https://doi.org/10.5194/amt-15-3663-2022, https://doi.org/10.5194/amt-15-3663-2022, 2022
Short summary
Short summary
Ceilometers are instruments that are widely deployed as part of operational networks. They are usually not able to detect cloud phase. Here, we propose an evaluation of various methods to detect supercooled liquid water with ceilometer observations, using an extensive dataset from Davis, Antarctica. Our results highlight the possibility for ceilometers to detect supercooled liquid water in clouds.
Alex R. Aves, Laura E. Revell, Sally Gaw, Helena Ruffell, Alex Schuddeboom, Ngaire E. Wotherspoon, Michelle LaRue, and Adrian J. McDonald
The Cryosphere, 16, 2127–2145, https://doi.org/10.5194/tc-16-2127-2022, https://doi.org/10.5194/tc-16-2127-2022, 2022
Short summary
Short summary
This study confirms the presence of microplastics in Antarctic snow, highlighting the extent of plastic pollution globally. Fresh snow was collected from Ross Island, Antarctica, and subsequent analysis identified an average of 29 microplastic particles per litre of melted snow. The most likely source of these airborne microplastics is local scientific research stations; however, modelling shows their origin could have been up to 6000 km away.
Catherine Hardacre, Jane P. Mulcahy, Richard J. Pope, Colin G. Jones, Steven T. Rumbold, Can Li, Colin Johnson, and Steven T. Turnock
Atmos. Chem. Phys., 21, 18465–18497, https://doi.org/10.5194/acp-21-18465-2021, https://doi.org/10.5194/acp-21-18465-2021, 2021
Short summary
Short summary
We investigate UKESM1's ability to represent the sulfur (S) cycle in the recent historical period. The S cycle is a key driver of historical radiative forcing. Earth system models such as UKESM1 should represent the S cycle well so that we can have confidence in their projections of future climate. We compare UKESM1 to observations of sulfur compounds, finding that the model generally performs well. We also identify areas for UKESM1’s development, focussing on how SO2 is removed from the air.
Anthony C. Jones, Adrian Hill, Samuel Remy, N. Luke Abraham, Mohit Dalvi, Catherine Hardacre, Alan J. Hewitt, Ben Johnson, Jane P. Mulcahy, and Steven T. Turnock
Atmos. Chem. Phys., 21, 15901–15927, https://doi.org/10.5194/acp-21-15901-2021, https://doi.org/10.5194/acp-21-15901-2021, 2021
Short summary
Short summary
Ammonium nitrate is hard to model because it forms and evaporates rapidly. One approach is to relate its equilibrium concentration to temperature, humidity, and the amount of nitric acid and ammonia gases. Using this approach, we limit the rate at which equilibrium is reached using various condensation rates in a climate model. We show that ammonium nitrate concentrations are highly sensitive to the condensation rate. Our results will help improve the representation of nitrate in climate models.
James Weber, Scott Archer-Nicholls, Nathan Luke Abraham, Youngsub M. Shin, Thomas J. Bannan, Carl J. Percival, Asan Bacak, Paulo Artaxo, Michael Jenkin, M. Anwar H. Khan, Dudley E. Shallcross, Rebecca H. Schwantes, Jonathan Williams, and Alex T. Archibald
Geosci. Model Dev., 14, 5239–5268, https://doi.org/10.5194/gmd-14-5239-2021, https://doi.org/10.5194/gmd-14-5239-2021, 2021
Short summary
Short summary
The new mechanism CRI-Strat 2 features state-of-the-art isoprene chemistry not previously available in UKCA and improves UKCA's ability to reproduce observed concentrations of isoprene, monoterpenes, and OH in tropical regions. The enhanced ability to model isoprene, the most widely emitted non-methane volatile organic compound (VOC), will allow understanding of how isoprene and other biogenic VOCs affect atmospheric composition and, through biosphere–atmosphere feedbacks, climate change.
Stefanie Kremser, Mike Harvey, Peter Kuma, Sean Hartery, Alexia Saint-Macary, John McGregor, Alex Schuddeboom, Marc von Hobe, Sinikka T. Lennartz, Alex Geddes, Richard Querel, Adrian McDonald, Maija Peltola, Karine Sellegri, Israel Silber, Cliff S. Law, Connor J. Flynn, Andrew Marriner, Thomas C. J. Hill, Paul J. DeMott, Carson C. Hume, Graeme Plank, Geoffrey Graham, and Simon Parsons
Earth Syst. Sci. Data, 13, 3115–3153, https://doi.org/10.5194/essd-13-3115-2021, https://doi.org/10.5194/essd-13-3115-2021, 2021
Short summary
Short summary
Aerosol–cloud interactions over the Southern Ocean are poorly understood and remain a major source of uncertainty in climate models. This study presents ship-borne measurements, collected during a 6-week voyage into the Southern Ocean in 2018, that are an important supplement to satellite-based measurements. For example, these measurements include data on low-level clouds and aerosol composition in the marine boundary layer, which can be used in climate model evaluation efforts.
John Staunton-Sykes, Thomas J. Aubry, Youngsub M. Shin, James Weber, Lauren R. Marshall, Nathan Luke Abraham, Alex Archibald, and Anja Schmidt
Atmos. Chem. Phys., 21, 9009–9029, https://doi.org/10.5194/acp-21-9009-2021, https://doi.org/10.5194/acp-21-9009-2021, 2021
Vidya Varma, Olaf Morgenstern, Kalli Furtado, Paul Field, and Jonny Williams
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-438, https://doi.org/10.5194/acp-2021-438, 2021
Revised manuscript not accepted
Short summary
Short summary
We introduce a simple parametrisation whereby the immersion freezing temperature in the model is linked to the mineral dust distribution through a diagnostic function, thus invoking regional differences in the nucleation temperatures instead of the global default value of −10 °C. This provides a functionality to mimic the role of Ice Nucleating Particles in the atmosphere on influencing the short-wave radiation over the Southern Ocean region by impacting the cloud phase.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
Short summary
Short summary
MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
Dongqi Lin, Basit Khan, Marwan Katurji, Leroy Bird, Ricardo Faria, and Laura E. Revell
Geosci. Model Dev., 14, 2503–2524, https://doi.org/10.5194/gmd-14-2503-2021, https://doi.org/10.5194/gmd-14-2503-2021, 2021
Short summary
Short summary
We present an open-source toolbox WRF4PALM, which enables weather dynamics simulation within urban landscapes. WRF4PALM passes meteorological information from the popular Weather Research and Forecasting (WRF) model to the turbulence-resolving PALM model system 6.0. WRF4PALM can potentially extend the use of WRF and PALM with realistic boundary conditions to any part of the world. WRF4PALM will help study air pollution dispersion, wind energy prospecting, and high-impact wind forecasting.
Paul T. Griffiths, Lee T. Murray, Guang Zeng, Youngsub Matthew Shin, N. Luke Abraham, Alexander T. Archibald, Makoto Deushi, Louisa K. Emmons, Ian E. Galbally, Birgit Hassler, Larry W. Horowitz, James Keeble, Jane Liu, Omid Moeini, Vaishali Naik, Fiona M. O'Connor, Naga Oshima, David Tarasick, Simone Tilmes, Steven T. Turnock, Oliver Wild, Paul J. Young, and Prodromos Zanis
Atmos. Chem. Phys., 21, 4187–4218, https://doi.org/10.5194/acp-21-4187-2021, https://doi.org/10.5194/acp-21-4187-2021, 2021
Short summary
Short summary
We analyse the CMIP6 Historical and future simulations for tropospheric ozone, a species which is important for many aspects of atmospheric chemistry. We show that the current generation of models agrees well with observations, being particularly successful in capturing trends in surface ozone and its vertical distribution in the troposphere. We analyse the factors that control ozone and show that they evolve over the period of the CMIP6 experiments.
Peter Sherman, Meng Gao, Shaojie Song, Alex T. Archibald, Nathan Luke Abraham, Jean-François Lamarque, Drew Shindell, Gregory Faluvegi, and Michael B. McElroy
Atmos. Chem. Phys., 21, 3593–3605, https://doi.org/10.5194/acp-21-3593-2021, https://doi.org/10.5194/acp-21-3593-2021, 2021
Short summary
Short summary
The aims here are to assess the role of aerosols in India's monsoon precipitation and to determine the relative contributions from Chinese and Indian emissions using CMIP6 models. We find that increased sulfur emissions reduce precipitation, which is primarily dynamically driven due to spatial shifts in convection over the region. A significant increase in precipitation (up to ~ 20 %) is found only when both Indian and Chinese sulfate emissions are regulated.
Fiona M. O'Connor, N. Luke Abraham, Mohit Dalvi, Gerd A. Folberth, Paul T. Griffiths, Catherine Hardacre, Ben T. Johnson, Ron Kahana, James Keeble, Byeonghyeon Kim, Olaf Morgenstern, Jane P. Mulcahy, Mark Richardson, Eddy Robertson, Jeongbyn Seo, Sungbo Shim, João C. Teixeira, Steven T. Turnock, Jonny Williams, Andrew J. Wiltshire, Stephanie Woodward, and Guang Zeng
Atmos. Chem. Phys., 21, 1211–1243, https://doi.org/10.5194/acp-21-1211-2021, https://doi.org/10.5194/acp-21-1211-2021, 2021
Short summary
Short summary
This paper calculates how changes in emissions and/or concentrations of different atmospheric constituents since the pre-industrial era have altered the Earth's energy budget at the present day using a metric called effective radiative forcing. The impact of land use change is also assessed. We find that individual contributions do not add linearly, and different Earth system interactions can affect the magnitude of the calculated effective radiative forcing.
Gillian Thornhill, William Collins, Dirk Olivié, Ragnhild B. Skeie, Alex Archibald, Susanne Bauer, Ramiro Checa-Garcia, Stephanie Fiedler, Gerd Folberth, Ada Gjermundsen, Larry Horowitz, Jean-Francois Lamarque, Martine Michou, Jane Mulcahy, Pierre Nabat, Vaishali Naik, Fiona M. O'Connor, Fabien Paulot, Michael Schulz, Catherine E. Scott, Roland Séférian, Chris Smith, Toshihiko Takemura, Simone Tilmes, Kostas Tsigaridis, and James Weber
Atmos. Chem. Phys., 21, 1105–1126, https://doi.org/10.5194/acp-21-1105-2021, https://doi.org/10.5194/acp-21-1105-2021, 2021
Short summary
Short summary
We find that increased temperatures affect aerosols and reactive gases by changing natural emissions and their rates of removal from the atmosphere. Changing the composition of these species in the atmosphere affects the radiative budget of the climate system and therefore amplifies or dampens the climate response of climate models of the Earth system. This study found that the largest effect is a dampening of climate change as warmer temperatures increase the emissions of cooling aerosols.
Peter Kuma, Adrian J. McDonald, Olaf Morgenstern, Richard Querel, Israel Silber, and Connor J. Flynn
Geosci. Model Dev., 14, 43–72, https://doi.org/10.5194/gmd-14-43-2021, https://doi.org/10.5194/gmd-14-43-2021, 2021
Jane P. Mulcahy, Colin Johnson, Colin G. Jones, Adam C. Povey, Catherine E. Scott, Alistair Sellar, Steven T. Turnock, Matthew T. Woodhouse, Nathan Luke Abraham, Martin B. Andrews, Nicolas Bellouin, Jo Browse, Ken S. Carslaw, Mohit Dalvi, Gerd A. Folberth, Matthew Glover, Daniel P. Grosvenor, Catherine Hardacre, Richard Hill, Ben Johnson, Andy Jones, Zak Kipling, Graham Mann, James Mollard, Fiona M. O'Connor, Julien Palmiéri, Carly Reddington, Steven T. Rumbold, Mark Richardson, Nick A. J. Schutgens, Philip Stier, Marc Stringer, Yongming Tang, Jeremy Walton, Stephanie Woodward, and Andrew Yool
Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, https://doi.org/10.5194/gmd-13-6383-2020, 2020
Short summary
Short summary
Aerosols are an important component of the Earth system. Here, we comprehensively document and evaluate the aerosol schemes as implemented in the physical and Earth system models, HadGEM3-GC3.1 and UKESM1. This study provides a useful characterisation of the aerosol climatology in both models, facilitating the understanding of the numerous aerosol–climate interaction studies that will be conducted for CMIP6 and beyond.
James Weber, Scott Archer-Nicholls, Paul Griffiths, Torsten Berndt, Michael Jenkin, Hamish Gordon, Christoph Knote, and Alexander T. Archibald
Atmos. Chem. Phys., 20, 10889–10910, https://doi.org/10.5194/acp-20-10889-2020, https://doi.org/10.5194/acp-20-10889-2020, 2020
Short summary
Short summary
Highly oxygenated organic molecules (HOMs) are important for aerosol growth and new particle formation, particularly in air masses with less sulphuric acid. This new chemical mechanism reproduces measured [HOM] and [HOM precursors] and is concise enough for use in global climate models. The mechanism also reproduces the observed suppression of HOMs by isoprene, suggesting enhanced emissions may not necessarily lead to more aerosols. Greater HOM importance in the pre-industrial era is also shown.
Cited articles
Anderson, T., Spall, S., Yool, A., Cipollini, P., Challenor, P., and Fasham, M.: Global fields of sea surface dimethylsulfide predicted from chlorophyll, nutrients and light, J. Mar. Syst., 30, 1–20, https://doi.org/10.1016/S0924-7963(01)00028-8, 2001. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Bates, T. S., Cline, J. D., Gammon, R. H., and Kelly-Hansen, S. R.: Regional and seasonal variations in the flux of oceanic dimethylsulfide to the atmosphere, J. Geophys. Res.-Oceans, 92, 2930–2938, 1987. a
Behrens, E. and Bostock, H.: The Response of the Subtropical Front to Changes in the Southern Hemisphere Westerly Winds—Evidence From Models and Observations, J. Geophys. Res.-Oceans, 128, e2022JC019139, https://doi.org/10.1029/2022JC019139, 2023. a
Bell, T. G., De Bruyn, W., Miller, S. D., Ward, B., Christensen, K. H., and Saltzman, E. S.: Air–sea dimethylsulfide (DMS) gas transfer in the North Atlantic: evidence for limited interfacial gas exchange at high wind speed, Atmos. Chem. Phys., 13, 11073–11087, https://doi.org/10.5194/acp-13-11073-2013, 2013. a
Bell, T. G., Porter, J. G., Wang, W.-L., Lawler, M. J., Boss, E., Behrenfeld, M. J., and Saltzman, E. S.: Predictability of Seawater DMS During the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES), Front. Mar. Sci., 7, 596763, https://doi.org/10.3389/fmars.2020.596763, 2021. a, b, c
Belviso, S., Bopp, L., Moulin, C., Orr, J. C., Anderson, T., Aumont, O., Chu, S., Elliott, S., Maltrud, M. E., and Simó, R.: Comparison of global climatological maps of sea surface dimethyl sulfide, Global Biogeochem. Cy., 18, 3, https://doi.org/10.1029/2003GB002193, 2004. a
Belviso, S., Masotti, I., Tagliabue, A., Bopp, L., Brockmann, P., Fichot, C., Caniaux, G., Prieur, L., Ras, J., Uitz, J., Loisel, H., Dessailly, D., Alvain, S., Kasamatsu, N., and Fukuchi, M.: DMS dynamics in the most oligotrophic subtropical zones of the global ocean, Biogeochemistry, 110, 215–241, https://doi.org/10.1007/s10533-011-9648-1, 2012. a
Bennartz, R. and Rausch, J.: Global and regional estimates of warm cloud droplet number concentration based on 13 years of AQUA-MODIS observations, Atmos. Chem. Phys., 17, 9815–9836, https://doi.org/10.5194/acp-17-9815-2017, 2017. a, b
Bhatti, Y. A., Revell, L. E., and McDonald, A. J.: Influences of Antarctic ozone depletion on Southern Ocean aerosols, J. Geophys. Res.-Atmos., 127, e2022JD037199, https://doi.org/10.1029/2022JD037199, 2022. a
Blomquist, B. W., Brumer, S. E., Fairall, C. W., Huebert, B. J., Zappa, C. J., Brooks, I. M., Yang, M., Bariteau, L., Prytherch, J., Hare, J. E., and others: Wind speed and sea state dependencies of air-sea gas transfer: Results from the High Wind speed Gas exchange Study (HiWinGS), J. Geophys. Res.-Oceans, 122, 8034–8062, 2017. a, b, c, d, e, f, g, h, i, j, k, l, m
Bock, J., Michou, M., Nabat, P., Abe, M., Mulcahy, J. P., Olivié, D. J. L., Schwinger, J., Suntharalingam, P., Tjiputra, J., van Hulten, M., Watanabe, M., Yool, A., and Séférian, R.: Evaluation of ocean dimethylsulfide concentration and emission in CMIP6 models, Biogeosciences, 18, 3823–3860, https://doi.org/10.5194/bg-18-3823-2021, 2021. a, b, c, d, e, f, g, h, i, j, k
Boyd, P. W.: Cruise data inventory from the R/V Tangaroa 61TG_3052 cruise in the Southern Ocean during 1999 (SOIREE project), Biological and Chemical Oceanography Data Management Office (BCO-DMO) [data set], (Version 17 Sept 2009) Version Date: 17 September 2009, http://lod.bco-dmo.org/id/dataset/3212 (last access: 4 December 2019), 2009. a
Boyd, P. W. and Law, C. S.: The Southern Ocean Iron RElease Experiment (SOIREE) - introduction and summary, Deep Sea Res. Part II, 48, 2425–2438, https://doi.org/10.1016/S0967-0645(01)00002-9, 2001. a, b
Bracegirdle, T., Holmes, C., Hosking, J., Marshall, G., Osman, M., Patterson, M., and Rackow, T.: Improvements in circumpolar Southern Hemisphere extratropical atmospheric circulation in CMIP6 compared to CMIP5, Earth Space Sci., 7, e2019EA001065, https://doi.org/10.1029/2019EA001065, 2020. a
Browning, T. J., Stone, K., Bouman, H. A., Mather, T. A., Pyle, D. M., Moore, C. M., and Martinez-Vicente, V.: Volcanic ash supply to the surface ocean—remote sensing of biological responses and their wider biogeochemical significance, Front. Mar. Sci., 2, 14, https://doi.org/10.3389/fmars.2015.00014, 2015. a
Choudhury, G. and Tesche, M.: A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements, Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, 2023. a, b
Curson, A. R. J., Todd, J. D., Sullivan, M. J., and Johnston, A. W. B.: Catabolism of dimethylsulphoniopropionate: microorganisms, enzymes and genes, Nat. Rev. Microbiol., 9, 849–859, https://doi.org/10.1038/nrmicro2653, 2011. a
Deppeler, S. L. and Davidson, A. T.: Southern Ocean phytoplankton in a changing climate, Front. Mar. Sci., 4, 40, https://doi.org/10.3389/fmars.2017.00040, 2017. a, b, c
EBAS: Convention on Long-Range Transboundary Air Pollution, United Nations Economic Commission for Europe. EBAS Database EMEP Framework – European Monitoring and Evaluation Programme PM2.5 and PM10 Data 1999–2014, https://ebas-data.nilu.no/ (last access: 17 May 2023), 2023. a
Elliott, S.: Dependence of DMS global sea-air flux distribution on transfer velocity and concentration field type, J. Geophys. Res.-Biogeosci., 114, 2156–2202, https://doi.org/10.1029/2008JG000710, 2009. a
Fairall, C., Yang, M., Bariteau, L., Edson, J., Helmig, D., McGillis, W., Pezoa, S., Hare, J., Huebert, B., and Blomquist, B.: Implementation of the Coupled Ocean-Atmosphere Response Experiment flux algorithm with CO2, dimethyl sulfide, and O3, J. Geophys. Res.-Oceans, 116, C4, https://doi.org/10.1029/2010JC006884 , 2011. a
Galí, M., Devred, E., Babin, M., and Levasseur, M.: Decadal increase in Arctic dimethylsulfide emission, P. Natl. Acad. Sci. USA, 116, 19311–19317, 2019. a
Galí, M., Lizotte, M., Kieber, D. J., Randelhoff, A., Hussherr, R., Xue, L., Dinasquet, J., Babin, M., Rehm, E., and Levasseur, M.: DMS emissions from the Arctic marginal ice zone, Elem. Sci. Anth., 9, 00113, https://doi.org/10.1525/elementa.2020.00113, 2021. a, b
Gregg, W. W. and Casey, N. W.: Sampling biases in MODIS and SeaWiFS ocean chlorophyll data, Remote Sens. Environ., 111, 25–35, 2007. a
Gros, V., Bonsang, B., Sarda-Estève, R., Nikolopoulos, A., Metfies, K., Wietz, M., and Peeken, I.: Concentrations of dissolved dimethyl sulfide (DMS), methanethiol and other trace gases in context of microbial communities from the temperate Atlantic to the Arctic Ocean, Biogeosciences, 20, 851–867, https://doi.org/10.5194/bg-20-851-2023, 2023. a, b
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M. D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M., Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A., Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D., Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K., Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang, Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives, Rev. Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018. a, b, c
Gryspeerdt, E., McCoy, D. T., Crosbie, E., Moore, R. H., Nott, G. J., Painemal, D., Small-Griswold, J., Sorooshian, A., and Ziemba, L.: The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data, Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, 2022. 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
Hayashida, H., Carnat, G., Galí, M., Monahan, A. H., Mortenson, E., Sou, T., and Steiner, N. S.: Spatiotemporal variability in modeled bottom ice and sea surface dimethylsulfide concentrations and fluxes in the Arctic during 1979–2015, Global Biogeochem. Cy., 34, e2019GB006456, https://doi.org/10.1029/2019GB006456, 2020. a
Haëntjens, N., Boss, E., and Talley, L. D.: Revisiting O cean C olor algorithms for chlorophyll a and particulate organic carbon in the S outhern O cean using biogeochemical floats, J. Geophys. Res.-Oceans, 122, 6583–6593, 2017. a
Hu, C., Feng, L., Lee, Z., Franz, B. A., Bailey, S. W., Werdell, P. J., and Proctor, C. W.: Improving satellite global chlorophyll a data products through algorithm refinement and data recovery, J. Geophys. Res.-Oceans, 124, 1524–1543, 2019. a
Huebert, B. J., Blomquist, B. W., Yang, M. X., Archer, S. D., Nightingale, P. D., Yelland, M. J., Stephens, J., Pascal, R. W., and Moat, B. I.: Linearity of DMS transfer coefficient with both friction velocity and wind speed in the moderate wind speed range, Geophys. Res. Lett., 37, 1, https://doi.org/10.1029/2009GL041203, 2010. a
Hulswar, S., Simó, R., Galí, M., Bell, T. G., Lana, A., Inamdar, S., Halloran, P. R., Manville, G., and Mahajan, A. S.: Third revision of the global surface seawater dimethyl sulfide climatology (DMS-Rev3), Earth Syst. Sci. Data, 14, 2963–2987, https://doi.org/10.5194/essd-14-2963-2022, 2022. a, b, c, d, e, f
Jarníková, T. and Tortell, P. D.: Towards a revised climatology of summertime dimethylsulfide concentrations and sea–air fluxes in the Southern Ocean, Environ. Chem., 13, 364, https://doi.org/10.1071/EN14272, 2016. a, b
Jena, B.: The effect of phytoplankton pigment composition and packaging on the retrieval of chlorophyll-a concentration from satellite observations in the Southern Ocean, Int. J. Remote Sens., 38, 3763–3784, 2017. a
Johnson, R., Strutton, P. G., Wright, S. W., McMinn, A., and Meiners, K. M.: Three improved satellite chlorophyll algorithms for the Southern Ocean, J. Geophys. Res.-Oceans, 118, 3694–3703, 2013. a
Keller, M. D., Bellows, W. K., and Guillard, R. L.: Dimethyl Sulfide Production in Marine Phytoplankton, Bio. Sulfur Environ., 393, 167–182, https://doi.org/10.1021/bk-1989-0393.ch011, 1989. a
Kettle, A., Andreae, M. O., Amouroux, D., Andreae, T., Bates, T., Berresheim, H., Bingemer, H., Boniforti, R., Curran, M., DiTullio, G., and others: A global database of sea surface dimethylsulfide (DMS) measurements and a procedure to predict sea surface DMS as a function of latitude, longitude, and month, Global Biogeochem. Cy., 13, 399–444, 1999. a
Kiene, R. P. and Bates, T. S.: Biological removal of dimethyl sulphide from sea water, Nature, 345, 702–705, 1990. a
Kloster, S., Feichter, J., Maier-Reimer, E., Six, K. D., Stier, P., and Wetzel, P.: DMS cycle in the marine ocean-atmosphere system – a global model study, Biogeosciences, 3, 29–51, https://doi.org/10.5194/bg-3-29-2006, 2006. a
Korhonen, H., Carslaw, K. S., Spracklen, D. V., Mann, G. W., and Woodhouse, M. T.: Influence of oceanic dimethyl sulfide emissions on cloud condensation nuclei concentrations and seasonality over the remote Southern Hemisphere oceans: A global model study, J. Geophys. Res.-Atmos., 113, D15, https://doi.org/10.1029/2007jd009718, 2008. a
Kremser, S., Harvey, M., Kuma, P., Hartery, S., Saint-Macary, A., McGregor, J., Schuddeboom, A., von Hobe, M., Lennartz, S. T., Geddes, A., Querel, R., McDonald, A., Peltola, M., Sellegri, K., Silber, I., Law, C. S., Flynn, C. J., Marriner, A., Hill, T. C. J., DeMott, P. J., Hume, C. C., Plank, G., Graham, G., and Parsons, S.: Southern Ocean cloud and aerosol data: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage, Earth Syst. Sci. Data, 13, 3115–3153, https://doi.org/10.5194/essd-13-3115-2021, 2021. a, b, c, d
Kuma, P., McDonald, A. J., Morgenstern, O., Alexander, S. P., Cassano, J. J., Garrett, S., Halla, J., Hartery, S., Harvey, M. J., Parsons, S., Plank, G., Varma, V., and Williams, J.: Evaluation of Southern Ocean cloud in the HadGEM3 general circulation model and MERRA-2 reanalysis using ship-based observations, Atmos. Chem. Phys., 20, 6607–6630, https://doi.org/10.5194/acp-20-6607-2020, 2020. a
Lana, A., Bell, T., Simó, R., Vallina, S., Ballabrera-Poy, J., Kettle, A., Dachs, J., Bopp, L., Saltzman, E., Stefels, J., and others: An updated climatology of surface dimethlysulfide concentrations and emission fluxes in the global ocean, Global Biogeochem. Cy., 25, 1–17, 2011. a, b, c, d, e, f, g, h, i, j
Law, C. S., Smith, M. J., Harvey, M. J., Bell, T. G., Cravigan, L. T., Elliott, F. C., Lawson, S. J., Lizotte, M., Marriner, A., McGregor, J., Ristovski, Z., Safi, K. A., Saltzman, E. S., Vaattovaara, P., and Walker, C. F.: Overview and preliminary results of the Surface Ocean Aerosol Production (SOAP) campaign, Atmos. Chem. Phys., 17, 13645–13667, https://doi.org/10.5194/acp-17-13645-2017, 2017. a
Lee, G., Park, J., Jang, Y., Lee, M., Kim, K.-R., Oh, J.-R., Kim, D., Yi, H.-I., and Kim, T.-Y.: Vertical variability of seawater DMS in the South Pacific Ocean and its implication for atmospheric and surface seawater DMS, Chemosphere, 78, 1063–1070, 2010. a
Longman, J., Palmer, M. R., Gernon, T. M., Manners, H. R., and Jones, M. T.: Subaerial volcanism is a potentially major contributor to oceanic iron and manganese cycles, Commun. Earth Environ., 3, 60, https://doi.org/10.1038/s43247-022-00389-7, 2022. a
Matrai, P. A., Balch, W. M., Cooper, D. J., and Saltzman, E. S.: Ocean color and atmospheric dimethyl sulfide: on their mesoscale variability, J. Geophys. Res.-Atmos., 98, 23469–23476, 1993. a
Mulcahy, J. P., Johnson, C., Jones, C. G., Povey, A. C., Scott, C. E., Sellar, A., Turnock, S. T., Woodhouse, M. T., Abraham, N. L., Andrews, M. B., Bellouin, N., Browse, J., Carslaw, K. S., Dalvi, M., Folberth, G. A., Glover, M., Grosvenor, D. P., Hardacre, C., Hill, R., Johnson, B., Jones, A., Kipling, Z., Mann, G., Mollard, J., O'Connor, F. M., Palmiéri, J., Reddington, C., Rumbold, S. T., Richardson, M., Schutgens, N. A. J., Stier, P., Stringer, M., Tang, Y., Walton, J., Woodward, S., and Yool, A.: Description and evaluation of aerosol in UKESM1 and HadGEM3-GC3.1 CMIP6 historical simulations, Geosci. Model Dev., 13, 6383–6423, https://doi.org/10.5194/gmd-13-6383-2020, 2020. a, b, c, d, e, f
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. 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, edited byL Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2013. a
NeSI: New Zealand eScience Infrastructure, https://www.nesi.org.nz/ (last access: 20 October 2023), 2023. 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, https://doi.org/10.22033/ESGF/CMIP6.5016, 2019. a
O'Reilly, J. E. and Werdell, P. J.: Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6, Remote Sens. Environ., 229, 32–47, 2019. a
Pandis, S. N., Russell, L. M., and Seinfeld, J. H.: The relationship between DMS flux and CCN concentration in remote marine regions, J. Geophys. Res.-Atmos., 99, 16945–16957, 1994. a
Pithan, F., Athanase, M., Dahlke, S., Sánchez-Benítez, A., Shupe, M. D., Sledd, A., Streffing, J., Svensson, G., and Jung, T.: Nudging allows direct evaluation of coupled climate models with in-situ observations: A case study from the MOSAiC expedition, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-706, 2022. a
Platnick, S., Hubanks, P., Meyer, K., and King, M. D.: MODIS Atmosphere L3 Monthly Product (08_L3), NASA MODIS Adaptive Processing System, Goddard Space Flight Center, https://giovanni.gsfc.nasa.gov/ (last access: 23 May 2023), 2015. a
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua, IEEE Trans. Geosci. Remote Sens.: a publication of the IEEE Geosci. Remote Sens. Soc., 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017. a, b
Preunkert, S., Legrand, M., Jourdain, B., Moulin, C., Belviso, S., Kasamatsu, N., Fukuchi, M., and Hirawake, T.: Interannual variability of dimethylsulfide in air and seawater and its atmospheric oxidation by-products (methanesulfonate and sulfate) at Dumont d'Urville, coastal Antarctica (1999–2003), J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2006JD007585, 2007. a, b
Read, K. A., Lewis, A. C., Bauguitte, S., Rankin, A. M., Salmon, R. A., Wolff, E. W., Saiz-Lopez, A., Bloss, W. J., Heard, D. E., Lee, J. D., and Plane, J. M. C.: DMS and MSA measurements in the Antarctic Boundary Layer: impact of BrO on MSA production, Atmos. Chem. Phys., 8, 2985–2997, https://doi.org/10.5194/acp-8-2985-2008, 2008. a, b, c
Revell, L. E., Kremser, S., Hartery, S., Harvey, M., Mulcahy, J. P., Williams, J., Morgenstern, O., McDonald, A. J., Varma, V., Bird, L., and Schuddeboom, A.: The sensitivity of Southern Ocean aerosols and cloud microphysics to sea spray and sulfate aerosol production in the HadGEM3-GA7.1 chemistry–climate model, Atmos. Chem. Phys., 19, 15447–15466, https://doi.org/10.5194/acp-19-15447-2019, 2019. a, b, c, d
Revell, L. E., Wotherspoon, N., Jones, O., Bhatti, Y. A., Williams, J., Mackie, S., and Mulcahy, J.: Atmosphere‐Ocean Feedback From Wind‐Driven Sea Spray Aerosol Production, Geophys. Res. Lett., 48, e2020GL091900, https://doi.org/10.1029/2020GL091900, 2021. a
Saltzman, E., King, D., Holmen, K., and Leck, C.: Experimental determination of the diffusion coefficient of dimethylsulfide in water, J. Geophys. Res.-Oceans, 98, 16481–16486, 1993. a
Salzmann, M., Ferrachat, S., Tully, C., Münch, S., Watson-Parris, D., Neubauer, D., Siegenthaler-Le Drian, C., Rast, S., Heinold, B., Crueger, T., and others: The Global Atmosphere-aerosol Model ICON-A-HAM2. 3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness, J. Adv. Model. Earth Syst., 14, e2021MS002699, https://doi.org/10.1029/2021MS002699, 2022. a
Santoso, A., Mcphaden, M. J., and Cai, W.: The defining characteristics of ENSO extremes and the strong 2015/2016 El Niño, Rev. Geophys., 55, 1079–1129, 2017. a
Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., Decharme, B., Delire, C., Berthet, S., Chevallier, M., and others: Evaluation of CNRM earth system model, CNRM-ESM2-1: Role of earth system processes in present-day and future climate, J. Adv. Model. Earth Syst., 11, 4182–4227, 2019. a
Seland, Ø., 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, 10, https://doi.org/10.22033/ESGF/CMIP6.8036, 2019. 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
Smith, M. J., Walker, C. F., Bell, T. G., Harvey, M. J., Saltzman, E. S., and Law, C. S.: Gradient flux measurements of sea–air DMS transfer during the Surface Ocean Aerosol Production (SOAP) experiment, Atmos. Chem. Phys., 18, 5861–5877, https://doi.org/10.5194/acp-18-5861-2018, 2018. a, b, c
Tang, W., Llort, J., Weis, J., Perron, M.M., Basart, S., Li, Z., Sathyendranath, S., Jackson, T., Sanz Rodriguez, E., Proemse, B. C., and Bowie, A. R.: Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires, Nature, 597, 370–375, 2021. 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, 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, https://doi.org/10.22033/ESGF/CMIP6.5603, 2018. a
Telford, P. J., Braesicke, P., Morgenstern, O., and Pyle, J. A.: Technical Note: Description and assessment of a nudged version of the new dynamics Unified Model, Atmos. Chem. Phys., 8, 1701–1712, https://doi.org/10.5194/acp-8-1701-2008, 2008. a
Thompson, P. A., Bonham, P., Thomson, P., Rochester, W., Doblin, M. A., Waite, A. M., Richardson, A., and Rousseaux, C. S.: Climate variability drives plankton community composition changes: The 2010–2011 El Niño to La Niña transition around Australia, J. Plankton Res., 37, 966–984, 2015. a
Tison, J.-L., Brabant, F., Dumont, I., and Stefels, J.: High-resolution dimethyl sulfide and dimethylsulfoniopropionate time series profiles in decaying summer first-year sea ice at Ice Station Polarstern, western Weddell Sea, Antarctica, J. Geophys. Res.-Biogeosci., 115, G4, https://doi.org/10.1029/2010JG001427, 2010. a
Tjiputra, J. F., Schwinger, J., Bentsen, M., Morée, A. L., Gao, S., Bethke, I., Heinze, C., Goris, N., Gupta, A., He, Y.-C., Olivié, D., Seland, Ø., and Schulz, M.: Ocean biogeochemistry in the Norwegian Earth System Model version 2 (NorESM2), Geosci. Model Dev., 13, 2393–2431, https://doi.org/10.5194/gmd-13-2393-2020, 2020. a, b
Uhlig, C., Damm, E., Peeken, I., Krumpen, T., Rabe, B., Korhonen, M., and Ludwichowski, K.-U.: Sea ice and water mass influence dimethylsulfide concentrations in the central Arctic Ocean, Front. Earth Sci., 7, 179, https://doi.org/10.3389/feart.2019.00179 , 2019. a, b, c
Wang, Y., Chen, H.-H., Tang, R., He, D., Lee, Z., Xue, H., Wells, M., Boss, E., and Chai, F.: Australian fire nourishes ocean phytoplankton bloom, Sci. Total Environ., 807, 150775, https://doi.org/10.1016/j.scitotenv.2021.150775, 2022. a
Wanninkhof, R.: Relationship between wind speed and gas exchange over the ocean, J. Geophys. Res.-Oceans, 97, 7373–7382, https://doi.org/10.1029/92JC00188, 1992. a, b
Wohl, C., Brown, I., Kitidis, V., Jones, A. E., Sturges, W. T., Nightingale, P. D., and Yang, M.: Underway seawater and atmospheric measurements of volatile organic compounds in the Southern Ocean, Biogeosciences, 17, 2593–2619, https://doi.org/10.5194/bg-17-2593-2020, 2020. a, b, c
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
Yang, M., Blomquist, B., Fairall, C., Archer, S., and Huebert, B.: Air‐sea exchange of dimethylsulfide in the Southern Ocean: Measurements from SO GasEx compared to temperate and tropical regions, J. Geophys. Res.-Oceans, 116, C4, https://doi.org/10.1029/2010JC006526, 2011. a, b, c
Yoder, J. A. and Kennelly, M. A.: Seasonal and ENSO variability in global ocean phytoplankton chlorophyll derived from 4 years of SeaWiFS measurements, Global Biogeochem. Cy., 17, 4, https://doi.org/10.1029/2002GB001942, 2003. a, b
Yool, A., Popova, E. E., and Anderson, T. R.: MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies, Geosci. Model Dev., 6, 1767–1811, https://doi.org/10.5194/gmd-6-1767-2013, 2013. a, b
Yool, A., Palmiéri, J., Jones, C., Sellar, A., de Mora, L., Kuhlbrodt, T., Popova, E., Mulcahy, J., Wiltshire, A., and Rumbold, S.: Spin‐up of UK Earth System Model 1 (UKESM1) for CMIP6, J. Adv. Model. Earth Syst., 12, e2019MS001933, https://doi.org/10.1029/2019MS001933, 2020. a, b
Yool, A., Palmiéri, J., Jones, C. G., de Mora, L., Kuhlbrodt, T., Popova, E. E., Nurser, A. J. G., Hirschi, J., Blaker, A. T., Coward, A. C., Blockley, E. W., and Sellar, A. A.: Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations, Geosci. Model Dev., 14, 3437–3472, https://doi.org/10.5194/gmd-14-3437-2021, 2021. a, b, c, d
Zeng, C., Xu, H., and Fischer, A. M.: Chlorophyll-a estimation around the Antarctica peninsula using satellite algorithms: hints from field water leaving reflectance, Sensors, 16, 2075, https://doi.org/10.3390/s16122075, 2016. a
Zhang, M., Park, K.-T., Yan, J., Park, K., Wu, Y., Jang, E., Gao, W., Tan, G., Wang, J., and Chen, L.: Atmospheric dimethyl sulfide and its significant influence on the sea-to-air flux calculation over the Southern Ocean, Prog. Oceanogr., 186, 102392, https://doi.org/10.1016/j.pocean.2020.102392, 2020. a
Short summary
Aerosols are a large source of uncertainty over the Southern Ocean. A dominant source of sulfate aerosol in this region is dimethyl sulfide (DMS), which is poorly simulated by climate models. We show the sensitivity of simulated atmospheric DMS to the choice of oceanic DMS data set and emission scheme. We show that oceanic DMS has twice the influence on atmospheric DMS than the emission scheme. Simulating DMS more accurately in climate models will help to constrain aerosol uncertainty.
Aerosols are a large source of uncertainty over the Southern Ocean. A dominant source of sulfate...
Altmetrics
Final-revised paper
Preprint