More Like this

1. Methanesulfonic acid measurements from a Greenland ice core (1850-2006)

   https://doi.org/10.18739/A2WW7717K  

   Jongebloed, Ursula; Alexander, Becky; Cole-Dai, Jihong (January 2023, Arctic Data Center)

   This dataset provides annual measurements of methanesulfonic acid (MSA) in Summit, Greenland ice core samples collected at variable resolution from 1200 to 2006. The abstract for the paper on these measurements is pasted below. The other measurements referenced in this abstract are published in Jongebloed et al. (2023) GRL, Jongebloed et al. (2023) ERL, and available in the Arctic Data Center (doi:10.18739/A26T0GX7K and doi:10.18739/A2N873162) Abstract: Marine phytoplankton are primary producers in ocean ecosystems and emit dimethyl sulfide (DMS) to the atmosphere. DMS emissions are the largest biological source of atmospheric sulfur and are one of the largest uncertainties in global climate modeling. DMS is oxidized to methanesulfonic acid (MSA), sulfur dioxide (SO2), and hydroperoxymethyl thioformate (HPMTF), all of which can be oxidized to sulfate. Ice core records of MSA are used to investigate past DMS emissions but rely on the implicit assumption that the relative yield of oxidation products from DMS remains constant. However, this assumption is uncertain because there are no long-term records that compare MSA to other DMS oxidation products. Here we share the first long-term record of both MSA and DMS-derived biogenic sulfate concentration in Greenland ice core samples from 1200 to 2006 CE. While MSA declines on average by 0.2 µg S kg-1 over the industrial era, biogenic sulfate from DMS increases by 0.8 µg S kg-1. This increasing biogenic sulfate contradicts previous assertions of declining North Atlantic primary productivity inferred from decreasing MSA concentrations in Greenland ice cores over the industrial era. The changing ratio of MSA to biogenic sulfate suggests that trends in MSA could be caused by time-varying atmospheric chemistry, and that MSA concentrations alone should not be used to infer past primary productivity. 

   more » « less
2. Industrial-era decline in Arctic methanesulfonic acid is offset by increased biogenic sulfate aerosol

   https://doi.org/10.1073/pnas.2307587120  

   Jongebloed, Ursula A.; Schauer, Andrew J.; Cole-Dai, Jihong; Larrick, Carleigh G.; Porter, William C.; Tashmim, Linia; Zhai, Shuting; Salimi, Sara; Edouard, Shana R.; Geng, Lei; et al (November 2023, Proceedings of the National Academy of Sciences)

   Marine phytoplankton are primary producers in ocean ecosystems and emit dimethyl sulfide (DMS) into the atmosphere. DMS emissions are the largest biological source of atmospheric sulfur and are one of the largest uncertainties in global climate modeling. DMS is oxidized to methanesulfonic acid (MSA), sulfur dioxide, and hydroperoxymethyl thioformate, all of which can be oxidized to sulfate. Ice core records of MSA are used to investigate past DMS emissions but rely on the implicit assumption that the relative yield of oxidation products from DMS remains constant. However, this assumption is uncertain because there are no long-term records that compare MSA to other DMS oxidation products. Here, we share the first long-term record of both MSA and DMS-derived biogenic sulfate concentration in Greenland ice core samples from 1200 to 2006 CE. While MSA declines on average by 0.2 µg S kg^–1over the industrial era, biogenic sulfate from DMS increases by 0.8 µg S kg^–1. This increasing biogenic sulfate contradicts previous assertions of declining North Atlantic primary productivity inferred from decreasing MSA concentrations in Greenland ice cores over the industrial era. The changing ratio of MSA to biogenic sulfate suggests that trends in MSA could be caused by time-varying atmospheric chemistry and that MSA concentrations alone should not be used to infer past primary productivity. 

   more » « less
3. Contribution of expanded marine sulfur chemistry to the seasonal variability of dimethyl sulfide oxidation products and size-resolved sulfate aerosol

   https://doi.org/10.5194/acp-24-3379-2024  

   Tashmim, Linia; Porter, William C; Chen, Qianjie; Alexander, Becky; Fite, Charles H; Holmes, Christopher D; Pierce, Jeffrey R; Croft, Betty; Ishino, Sakiko (January 2024, Atmospheric Chemistry and Physics)

   Abstract. Marine emissions of dimethyl sulfide (DMS) and the subsequent formation of its oxidation products methanesulfonic acid (MSA) and sulfuric acid (H2SO4) are well-known natural precursors of atmospheric aerosols, contributing to particle mass and cloud formation over ocean and coastal regions. Despite a long-recognized and well-studied role in the marine troposphere, DMS oxidation chemistry remains a work in progress within many current air quality and climate models, with recent advances exploring heterogeneous chemistry and uncovering previously unknown intermediate species. With the identification of additional DMS oxidation pathways and intermediate species that influence the eventual fate of DMS, it is important to understand the impact of these pathways on the overall sulfate aerosol budget and aerosol size distribution. In this work, we update and evaluate the DMS oxidation mechanism of the chemical transport model GEOS-Chem by implementing expanded DMS oxidation pathways in the model. These updates include gas- and aqueous-phase reactions, the formation of the intermediates dimethyl sulfoxide (DMSO) and methanesulfinic acid (MSIA), and cloud loss and aerosol uptake of the recently quantified intermediate hydroperoxymethyl thioformate (HPMTF). We find that this updated mechanism collectively decreases the global mean surface-layer gas-phase sulfur dioxide (SO2) mixing ratio by 40 % and enhances the sulfate aerosol (SO42-) mixing ratio by 17 %. We further perform sensitivity analyses exploring the contribution of cloud loss and aerosol uptake of HPMTF to the overall sulfur budget. Comparing modeled concentrations to available observations, we find improved biases relative to previous studies. To quantify the impacts of these chemistry updates on global particle size distributions and the mass concentration, we use the TwO-Moment Aerosol Sectional (TOMAS) aerosol microphysics module coupled to GEOS-Chem and find that changes in particle formation and growth affect the size distribution of aerosol. With this new DMS-oxidation scheme, the global annual mean surface-layer number concentration of particles with diameters smaller than 80 nm decreases by 16.8 %, with cloud loss processes related to HPMTF being mostly responsible for this reduction. However, the global annual mean number of particles larger than 80 nm (corresponding to particles capable of acting as cloud condensation nuclei, CCN) increases by 3.8 %, suggesting that the new scheme promotes seasonal particle growth to these sizes. 

   more » « less
4. Atmospheric particle abundance and sea salt aerosol observations in the springtime Arctic: a focus on blowing snow and leads

   https://doi.org/10.5194/acp-22-15263-2022  

   Chen, Qianjie; Mirrielees, Jessica A.; Thanekar, Sham; Loeb, Nicole A.; Kirpes, Rachel M.; Upchurch, Lucia M.; Barget, Anna J.; Lata, Nurun Nahar; Raso, Angela R.; McNamara, Stephen M.; et al (January 2022, Atmospheric Chemistry and Physics)

   Abstract. Sea salt aerosols play an important role in the radiationbudget and atmospheric composition over the Arctic, where the climate israpidly changing. Previous observational studies have shown that Arctic sea ice leads are an important source of sea salt aerosols, and modeling efforts have also proposed blowing snow sublimation as a source. In this study,size-resolved atmospheric particle number concentrations and chemicalcomposition were measured at the Arctic coastal tundra site ofUtqiaġvik, Alaska, during spring (3 April–7 May 2016). Blowing snow conditions were observed during 25 % of the 5-week study period andwere overpredicted by a commonly used blowing snow parameterization based solely on wind speed and temperature. Throughout the study, open leads werepresent locally. During periods when blowing snow was observed, significantincreases in the number concentrations of 0.01–0.06 µm particles(factor of 6, on average) and 0.06–0.3 µm particles (67 %, on average) and a significant decrease (82 %, on average) in 1–4 µmparticles were observed compared to low wind speed periods. These size distribution changes were likely caused by the generation of ultrafineparticles from leads and/or blowing snow, with scavenging of supermicronparticles by blowing snow. At elevated wind speeds, both submicron andsupermicron sodium and chloride mass concentrations were enhanced,consistent with wind-dependent local sea salt aerosol production. Atmoderate wind speeds below the threshold for blowing snow as well as during observed blowing snow, individual sea spray aerosol particles were measured.These individual salt particles were enriched in calcium relative to sodiumin seawater due to the binding of this divalent cation with organic matter in the sea surface microlayer and subsequent enrichment during seawaterbubble bursting. The chemical composition of the surface snowpack alsoshowed contributions from sea spray aerosol deposition. Overall, theseresults show the contribution of sea spray aerosol production from leads onboth aerosols and the surface snowpack. Therefore, if blowing snowsublimation contributed to the observed sea salt aerosol, the snow beingsublimated would have been impacted by sea spray aerosol deposition rather than upward brine migration through the snowpack. Sea spray aerosol production from leads is expected to increase, with thinning and fracturingof sea ice in the rapidly warming Arctic. 

   more » « less
5. Sea Ice Forecasting using Attention-based Ensemble LSTM

   Ali, Sahara; Huang, Yiyi; Huang, Xin; Wang, Jianwu (January 2021, Proceedings of Tackling Climate Change with Machine Learning Workshop at International Conference on Machine Learning (ICML 2021))

   Accurately forecasting Arctic sea ice from sub- seasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecast- ing. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife. 

   more » « less