DOMOS

Ongoing research project |
2021 to 2023

The Dust-Ocean Modelling & Observing Study (DOMOS) will advance the understanding of dust and ocean interactions in a changing climate through an innovative use of model and observations. The project will develop a new retrieval of dust deposition from satellite lidar data (CALIPSO and Aeolus), will validate the dust deposition field from the CAMS reanalysis and will also provide assimilation tests of IASI and Aeolus aerosol products with the goal of providing a better description of the dust aerosols, for applications in aerosol radiative impacts and ocean biogeochemistry. An improved representation of the physical and chemical characteristics of dust deposition over the ocean is crucial to interpret the observed climatic change responses and to better describe the future ones. This includes a better understanding and quantification of the deposition of soluble iron from natural and anthropogenic dust and of its contribution relative to biomass burning and anthropogenic aerosols which will be one of the main deliverables of the project. A scientific roadmap to highlight the findings of the project and identify possible gaps in the modelling and the observing approaches will also be provided. 

Background

The ocean plays a key role in climate by exchanging energy and climate-relevant gases with the atmosphere. According to the IPCC (AR5 2013 [1]), 90% of the total energy in excess in the atmosphere was absorbed by the ocean between 1971 and 2010. At the same time, gaseous CO2 is absorbed in the surface layer of the ocean and becomes available for the process of photosynthesis performed by microscopic phytoplankton cells. The resulting organic carbon compounds are transferred into the ocean’s interior through gravitational sinking (particulate), mixing and subduction (both particulate and dissolved). This combination of processes, known as biological carbon pump, together with another set of physical processes known as solubility carbon pump (Volk and Hoffert, 1985 [2]; Ito and Follows, 2003 [3]), contribute to slow down the increase of atmospheric CO2 that results from anthropogenic activities. Although only a fraction of primary production (5-20%) is ultimately exported to the deep ocean (Henson et al., 2019 [4]; Falkowski et al., 1998 [5]) several studies have documented the importance of primary production in modulating surface oceanic CO2 concentrations and, as a consequence, the exchange of CO2 between ocean and atmosphere (Falkowski et al., 2000 [6]; Hauck and Volker, 2015 [7]). Moreover, primary production is at the base of the marine food-web sustaining all marine life, including commercially relevant species for fisheries. Finally, the abundance and variability of phytoplankton in the open ocean have a dominant role in determining ocean colour and, as a consequence, the penetration of light in the water column. This in turn affects sea surface temperature resulting in a feedback potentially significant in determining the trajectory of tropical storms (Gnanadesikan et al., 2010 [8]).

Primary production is currently estimated from combinations of global ocean biogeochemical models and from satellite-based methods to be in the order of 30 - 70 Pg-C yr-1 (Carr et al., 2006 [9]; Anav et al., 2013 [10]) with spatial distribution depending, among other factors, on the input of nutrients from atmospheric sources (Krishnamurthy et al., 2010 [11]; Myriokefalitakis et al., 2020 [12]) . Among other species deposited onto the open ocean surface, nitrogen (N), phosphorus (P), silica (SiO₂), and iron (Fe) are the nutrients that can limit phytoplankton growth, directly impacting  marine productivity, ocean colour and the ocean’s capacity to absorb CO2. Among these, iron availability is the most important limiting factor for phytoplankton growth over large oceanic areas (Okin et al., 2011 [13]).

Iron concentrations in vast areas of the ocean are very low, due to the low solubility of iron in seawater (Boyd and Ellwood, 2010 [14]). Aeolian dust is the principal source (~95%) of Fe to the surface open ocean, followed by Fe-containing aerosols from biomass burning and fossil-fuel combustion emissions (e.g., Mahowald et al., 2009 [15]). In the Atlantic Ocean, Fe deposition from Saharan dust can drive significant variability in deep carbon export (Pabortsava et al., 2017 [16]). However, Fe can only be utilized by phytoplankton in its bioavailable (dissolved) form (e.g., aqueous, colloidal, or nanoparticulate). Although the essential role of iron in oceanic productivity is well established (Tagliabue et al., 2017 [17]), considerable uncertainty remains on the impact of atmospheric composition on phytoplankton Fe-limitations and consequently the oceanic carbon-cycle. Indeed, due to the role played by phytoplankton in the transfer of carbon dioxide into organic carbon and carbon sequestration, iron limitation likely plays a major role in the global carbon cycle. It has been also suggested that variations in oceanic primary productivity, spurred by changes in the deposition of iron in atmospheric dust, control atmospheric CO2 concentrations through a delicate balance, and hence global climate, over glacial-interglacial timescales (Street and Paytan, 2005 [18]). Another important biogeochemical parameter to characterize ocean productivity is marine nitrogen fixation, i.e., the reduction of gaseous N2 to ammonium performed by marine organisms. N2-fixing species (e.g., diazotrophs) have elevated Fe requirements and their growth may also be limited over large areas of the Atlantic ocean (Pabortsava et al., 2017 [19]; Schlosser et al, 2014 [20]).

Human activities have heavily perturbed [21] the atmospheric composition and, unavoidably, the atmospheric inputs to the global ocean. On the one side, dust aerosols are subject to atmospheric processing during their long-range transport, resulting in spatially variable solubility of nutrients and thus impacting the spatial distribution of marine productivity (e.g., Myriokefalitakis et al., 2020 [22]). A major mechanism leading to an increase of Fe solubility is acidic (proton-promoted) dissolution, with low pH conditions in aerosol water favouring Fe dissolution through the weakening of Fe-O bonds of Fe oxides, Fe hydroxides and aluminosilicates in dust (Johnson and Meskhidze, 2013 [23]). Also oxalate, can act as an organic ligand, enhancing Fe dissolution in aqueous solutions under moderately acidic conditions. The oxalate-mediated mechanisms for Fe(II) formation depend upon the availability of oxalic acid or oxalate compounds and are largely dependent on sunlight. On the other side, combustion Fe (from biomass burning and anthropogenic emissions), while a much smaller source of iron, is considerably more soluble than dust Fe and is estimated to contribute up to 50% of the bioavailable Fe deposition (Luo et al., 2008 [24]; Mahowald et al., 2009 [25]; Ito, 2015 [26]; Winton et al., 2015 [27]).

The contribution of dust emitted from anthropogenically perturbed land remains still subject to debate, with values ranging from 10% to at least 50%. Ginoux et al. (2012a) [28] used high-resolution MODIS radiances to identify sources where the atmospheric column is frequently dusty. Sources were attributed to cultivation according to a land use atlas (Goldewijk, 2001 [29]). Maps of both natural and cultivated sources were introduced into a dust transport model and the anthropogenic fraction of present-day dust emission was estimated to be ~25 %. The anthropogenic dust fraction is potentially important for the Fe cycle; there is evidence that dust aerosols created by human activity have a distinct composition from dust arising from natural sources. Ginoux et al. (2012b) [30] show that anthropogenic dust sources are coincident with high levels of atmospheric ammonia. Ammonia is a precursor to heterogeneous chemical reactions associated with the uptake of nitrate aerosols and ammonium salts onto the surface of dust particles. Cultivated regions are enriched in ammonium precursors due to the use of fertilizers and large numbers of livestock (Beusen et al., 2008 [31]), both of which are expected to increase in the coming century as the demand for food security increases with population. Cultivated sources are also expected to be enriched in Fe-bearing minerals including hematite and clays. This is because agriculture is practiced in regions where soil moisture is present during at least part of the year, and this moisture chemically weathers silicate minerals, creating clays along with Fe oxides and hydroxides like hematite and goethite.

Modelling studies estimate a global atmospheric dissolved Fe deposition flux into the ocean in the range 0.2–0.4 Tg-Fe yr-1 for present-day conditions (Myriokefalitakis et al., 2018 [32]; Ito et al., 2019 [33]),  a factor of ~2 higher than during the preindustrial era (e.g., Scanza et al., 2018 [34]). Models of the atmospheric iron cycle with different levels of complexity have been employed to simulate atmospheric Fe dissolution: from simple schemes including first order rate processing constants applied to a globally uniform 3.5% of Fe in dust, to more complex ones allowing different types of acidic species to interact with dust that account for mineral-specific dissolution rates and oxalate processing (e.g., Myriokefalitakis et al., 2018 [35]; Ito et al., 2019 [36]). However, most IPCC-class Earth System Models use simplified climatological representations of dust deposition and of its nutrients content and solubility (e.g. Aumont et al., 2015 [37]; Seland et al., 2020 [38]), although it is widely accepted that dust deposition is by nature highly episodic (Guieu et al., 2014 [39]). 

Therefore, a better representation of the mechanisms behind the spatial and temporal variability of atmosphere-ocean interactions is required. An improved representation of the physical and chemical characteristics of dust deposition over the ocean could be key to interpret the observed climatic change responses and to better describe the future ones. This includes a better understanding and quantification of the deposition of soluble iron from natural and anthropogenic dust and of its contribution relative to biomass burning and anthropogenic aerosols.

Figure 1

Figure 1. Dust processes, adapted from Ginoux P., Mineral Dust Cycle

Mineral dust is mechanically produced by surface winds breaking soil cohesion over surfaces with no vegetation and dry soil such as deserts. North Africa, including the Sahara and the Sahel, is the biggest producer of dust contributing to a ~46 % of the global emissions and a ~50% of the global dust loading and dust optical depth. (Kok et al., 2021 [40]). Dust is essentially composed of clay and silt soil particles, whose diameters vary between 0.1 to 20 micrometers. Larger particles are also found at large distances from the sources (van der Does et al., 2018 [41]). The lifetime of dust particles in the atmosphere is of the order of one week, over which period they can be transported several thousand kilometres by winds. Dust is removed from the atmosphere by wet deposition (i.e. scavenging through precipitation in the water or ice phase), dry deposition/gravitational settling and turbulent mixing in the Planetary Boundary Layer (PBL).  Dust aerosols have a big impact on the incoming solar radiation through scattering and on the outgoing terrestrial radiation through absorption. They also play an important role by acting as ice nuclei, particularly the bigger particles, therefore affecting cloud lifetime and optical properties (Figure 1).

2020 has seen one of the biggest dust events of the decade, in June 2020, with a huge amount of dust being transported from the Sahara to the tropical Atlantic Ocean. The dust can be clearly seen in visible satellite imagery, such as from the Suomi NPP/VIIRS imagery on 17 June 2020 (Figure 2). Some studies have shown that the dust transport over the Atlantic has instead decreased in recent years (Ridley et al, 2014 [42]), in connection to a weakening of surface winds possibly induced by changes in anthropogenic aerosol forcing in Western Africa.  A potential increase (decrease) in dust transport over the ocean, could make iron and other nutrients, such as silica (SiO₂) and phosphorus (P), more (less) available for phytoplankton, hence triggering changes in marine primary production and the biological carbon pump. This mechanism is relatively well understood from a theoretical point of view but has not been systematically shown with an integrated approach of modelling and satellite and in-situ observations. For example, one of the biggest unknowns remains the amount of dust which is actually deposited to the ocean. Some estimates based on reanalysis and satellite data indicate that 218 ± 48 Tg of dust is annually deposited into the Atlantic (Ridley et al, 2012 [43]).  However, the model timeseries used in the study only covered two years. Similarly, Yu et al. (2019) [44] on the basis of a ten-year (2007-2016) analysis of CALIOP, MODIS, MISR, and IASI observations, estimated the amount of dust deposited into the Tropical Atlantic Ocean at 136-222 Tg/year.  Reanalysis datasets which provide long-term series (2003 to present) of dust deposition based on modelled emissions and transport and assimilated satellite observations such as the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (Inness et al 2019 [45]) are available but have not yet been validated with independent observations of dust deposition over the ocean. Questions remain still open regarding closing the relationship between sources, long-range transport and deposition processes.

From the point of view of satellite-based Earth Observations to address the link between atmospheric composition, dynamics and ocean biogeochemistry, a significant amount of new information has become available through large programs such as the ESA’s Earth Explorer missions and its wind lidar mission Aeolus, and EU-ESA Copernicus missions such as Sentinel 5P, which is now producing a wealth of observations related to atmospheric composition as well as ocean colour variables. Additionally the ESA Climate Change Initiative (CCI) has funded new retrievals and long timeseries reprocessing of climate relevant products such as aerosol optical depth, land cover, ocean colour from various sensors. It has also promoted their use and exploitation among the climate community by establishing the Climate Model User Group (CMUG).

From 2012 to 2016, a Transatlantic Saharan dust-monitoring campaign was carried out using an array of both submarine sediment traps and ocean-surface dust-collecting buoys. The sediment traps collected material settling through the water column at 1200m water depth, which includes Saharan dust, marine organic matter, and fossil remains of phytoplankton living in the surface ocean. It was shown that there is a strong seasonality in Saharan dust deposition, and in the properties of the dust particles themselves; a clear downwind fining could be demonstrated and a strong difference in particle size with coarser-grained material deposited in summer as opposed to winter [46][47]. In addition, for the first time it was shown that aeolian dust particles are frequently ‘giant’ (>75µm) and can include individual quartz grains as large as 450µm [48]. The mechanisms playing a role in the emission, transport, and deposition (wet as well as dry) of Saharan dust are still far from understood, as are the marine environmental consequences of the dust deposition, although Guerreiro et al., 2017 [49], and 2019 [50] managed to show a relationship between dust deposition and response of opportunistic coccolithophorid species. However, the role of dust as a nutrient supplier to the ocean and its consecutive role as ballast material, which is required for a significant export of organic matter from the surface ocean to the deep, is still far from understood.

During the last two decades the amount of available observations of the ocean’s interior has tremendously increased thanks to the Argo program, which has fostered the deployment of autonomous floats across the global ocean. Some of these floats, known as BGC-Argo floats (Chai et al, 2020 [51]), are equipped with a set of biogeochemical sensors that make them able to measure, amongst others, Chl concentration, nitrate, pH or oxygen; on top of depth, temperature and salinity. BGC-Argo floats are particularly useful to study open ocean waters at any time of the year regardless of the sea conditions and to evaluate the impact of extreme and episodic atmospheric events, such as tropical or dust storms, on marine primary production (Chai et al, 2021 [52]).

The challenge is now to bring together all this wealth of observations with state-of-the-art models to provide a full 4D reconstruction of the ocean/atmosphere interactions, including aspects that are not directly observable.

Figure 2

Figure 2.  NASA Suomi NPP/VIIRS visible imagery showing Saharan dust over the tropical Atlantic on 17 June (right) 2020 (downloaded from NASA Worldview).

Objectives

DOMOS has the following technical objectives:

1. To collate available observations from ESA platforms and other sources and utilise/collect new ones from in-situ (mooring buoys, ground-based observing sites) and research vessels in order to provide a complete picture of dust deposition processes with focus over the Atlantic ocean.

2. To create a unique 4D-reconstruction of the dust full cycle including deposition based on the synergy of models and observations including vertical profiling through the use of advanced retrieval methods and of 4D-Var and Ensemble Kalman Filter analyses.

3. To advance our understanding of the trends in dust deposition over the Atlantic Ocean by exploiting observations, model simulations and existing atmospheric reanalysis.

4.  To generate and evaluate state-of-the-art model reconstructions of the atmospheric iron cycle and of its impact upon ocean biogeochemistry, including the contribution of anthropogenic and natural dust and other sources of soluble iron deposition.

5. To demonstrate the added value of this novel approach and identify any gaps in the observing system that need to be filled in order to have a complete picture of interactions between atmospheric dust and ocean. 

6. To provide a scientific roadmap and work in collaboration with Early Adopters and stakeholders with strong focus on scientific and technical inputs for actionable mitigation strategies as well as new EO science-based solutions. 

Science Questions

DOMOS aims to answer the following questions.

  1. To what extent dust deposition over the Atlantic has changed over the last 20 years? Can we identify robust trends in the reanalysis and model datasets and if yes, how can we verify them? Although estimates have been attempted before, there is the need to look at longer time-series such as those provided by atmospheric composition reanalysis and climate models and develop tailor-made satellite retrievals from multiple sensors and platforms, aimed at quantifying dust deposition. This is a challenge as dust deposition is not directly observable from satellite. Observations must be complemented with model-based information. Also, independent observations of dust deposition are needed to quantify the quality of the model-based and reanalysis-based reconstructions as well as to evaluate the performance of the bespoken satellite retrievals.
  2. What is the contribution of anthropogenic and natural sources of dust compared to biomass burning and anthropogenic aerosols to soluble iron deposition over the Atlantic? While dust is the largest contributor to total iron deposition by far, it is unclear what its contribution to soluble iron deposition is.
  3. What are the impacts of changes in dust deposition on marine biogeochemistry and their potential effects on ecosystems? The connection between changes in dust deposition and the nutrients available for marine ecosystems needs further investigation with a concerted synergy of modelling and observations. 

Workplan

DOMOS is comprised of 7 main work packages, as outlined below.

WBS

GANTT

DOMOS Consortium

European Centre for Medium-Range Weather Forecasts National Observatory of Athens Barcelona Supercomputing Center
Royal Netherlands Institute for Sea Research University of Cologne Norwegian Meteorological Institute

 

DOMOS Outputs

DOMOS is generating results of interest to the wider public and scientific community in particular:

Outline report of DOMOS project - summarising approach and the DOMOS products 

 

Related Projects

PRIMUS: Primary productivity in upwelling systems; PRIMUS is a multidisciplinary project funded through the European Space Agency (ESA)  

 

References

  1. https://www.ipcc.ch/report/ar5/syr
  2. Volk, T., & Hoffert, M. I. (1985). Ocean carbon pumps: analysis of relative strengths and efficiencies in ocean-driven atmospheric CO2 changes. In E. T. Sundquist, & W. S. Broecker (Eds.), The carbon cycle and atmospheric CO 2 : natural variations Archean to present. Chapman conference papers, 1984 (pp. 99-110). American Geophysical Union; Geophysical Monograph 32.
  3. Ito, T. and Follows, M. J. Upper ocean control on the solubility pump of CO2. Journal of Marine Research, 61 (4), 465-489, 2003. https://doi.org/10.1357/002224003322384898
  4. Henson, S., Le Moigne, F. and Giering, S.: Drivers of carbon export efficiency in the global Ocean, Global Biogeochem. Cy., 33 (7), doi:10.1029/2018GB006158, 2019.
  5. Falkowski, P.G., Barber, R.T. and Smetacek, V.: Biogeochemical controls and feedbacks on ocean primary production, Science, 281, 200-206, DOI: 10.1126/science.281.5374.200, 1998.
  6. Falkowski, P. G., et al.: The Global Carbon Cycle: A Test of Our Knowledge of Earth as a System, Science, 290(5490), 291–296, doi:10.1126/science.290.5490.291, 2000.
  7. Hauck, J., and C. Völker (2015), Rising atmospheric CO2 leads to large impact of biology on Southern Ocean CO2 uptake via changes of the Revelle factor, Geophys. Res. Lett., 42, 1459–1464, doi:10.1002/2015GL063070.
  8. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2010GL044514
  9. Carr. M.E., et al.: A comparison of global estimates of marine primary production from ocean color, Deep-Sea Res. II, 53, 741-770, doi:10.1016/j.dsr2.2006.01.028, 2006.
  10. Anav, A., et al.: Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models, J. Clim., 26 (18), DOI: https://doi.org/10.1175/JCLI-D-12-00417.1, 2013.
  11. Krishnamurthy, A., Moore, J. K., Zender, C. S. and Luo, C.: Effects of atmospheric inorganic nitrogen deposition on ocean biogeochemistry, J. Geophys. Res., 112(G2), G02019, doi:10.1029/2006JG000334, 2007.
  12. Myriokefalitakis, S., Gröger, M., Hieronymus, J. and Döscher, R.: An explicit estimate of the atmospheric nutrient impact on global oceanic productivity, Ocean Sci., 16(5), 1183–1205, doi:10.5194/os-16-1183-2020, 2020.
  13. Okin, G.et al.: Impacts of atmospheric nutrient deposition on marine productivity: Roles of nitrogen, phosphorus, and iron, Global Biogeochem. Cycles, 25(2), GB2022, doi:10.1029/2010GB003858, 2011.
  14. Boyd, P., Ellwood, M. The biogeochemical cycle of iron in the ocean. Nature Geosci 3, 675–682 (2010). https://doi.org/10.1038/ngeo964.
  15. Mahowald, N. M., Engelstaedter, S., Luo, C., Sealy, A., Ar- taxo, P., Benitez-Nelson, C., Bonnet, S., Chen, Y., Chuang, P. Y., Cohen, D. D., Dulac, F., Herut, B., Johansen, A. M., Kubilay, N., Losno, R., Maenhaut, W., Paytan, A., Prospero, J. M., Shank, L. M., and Siefert, R. L.: Atmo- spheric iron deposition: global distribution, variability, and human perturbations., Ann. Rev. Mar. Sci., 1, 245–278, https://doi.org/10.1146/annurev.marine.010908.163727, 2009.
  16. Pabortsava, Katsiaryna et al. 2017 Carbon sequestration in the deep Atlantic enhanced by Saharan dust. Nature Geoscience, 10 (3). 189-194.https://doi.org/10.1038/ngeo2899
  17. Tagliabue, A., Bowie, A. R., Boyd, P. W., Buck, K. N., Johnson, K. S. and Saito, M. A.: The integral role of iron in ocean biogeochemistry, Nature, 543(7643), 51–59, doi:10.1038/nature21058, 2017
  18. Met Ions Biol Syst, 2005;43:153-93. doi: 10.1201/9780824751999.ch7
  19. See reference 13.
  20. Schlosser, C., Klar, J. K., Wake, B. D., Snow, J. T., Honey, D. J., Woodward, E. M. S., Lohan, M. C., Achterberg, E. P. and Moore, C. M.: Seasonal ITCZ migration dynamically controls the location of the (sub)tropical Atlantic biogeochemical divide, Proc. Natl. Acad. Sci., 111(4), 1438–1442, doi:10.1073/pnas.1318670111, 2014.
  21. Johnson and Meskhidze (2013) Geosci. Model Dev., 6, 1137–1155.
  22. Myriokefalitakis, S., Gröger, M., Hieronymus, J. and Döscher, R.: An explicit estimate of the atmospheric nutrient impact on global oceanic productivity, Ocean Sci., 16(5), 1183–1205, doi:10.5194/os-16-1183-2020, 2020.
  23. Johnson and Meskhidze (2013) Geosci. Model Dev., 6, 1137–1155.
  24. Luo, C., Mahowald, N., Bond, T., Chuang, P.Y., Artaxo, P., Siefert, R., Chen, Y., Schauer, J., 2008. Combustion iron distribution and deposition. Global Biogeochemical Cycles 22. https://doi.org/10.1029/2007GB002964
  25. See reference 16.
  26. Ito, A., 2015. Atmospheric Processing of Combustion Aerosols as a Source of Bioavailable Iron. Environ. Sci. Technol. Lett. 2, 70–75. https://doi.org/10.1021/acs.estlett.5b00007.
  27. H. L. Winton, V.H.L. et al, Multiple sources of soluble atmospheric iron to Antarctic waters. Global Biogeochem. Cycles 30,421–437 (2016).
  28. Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. & Zhao, M. Global-scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products. Rev. Geophys. 50, RG3005 (2012b).
  29. Goldewijk K.K. Estimating global land use change over the past 300 years: The HYDE Database, 2001, https://doi.org/10.1029/1999GB001232.
  30. Ginoux et al. (2012b) Atmos. Chem. Phys., 12, 7351–7363.
  31. Beusen et al. (2008) Atmos. Environ., 42, 6067–6077.
  32. Myriokefalitakis, S., et al.: Reviews and syntheses: the GESAMP atmospheric iron deposition model intercomparison study, Biogeosciences, 15(21), 6659–6684, doi:10.5194/bg-15-6659-2018, 2018.
  33. Ito, A., et al: Pyrogenic iron: The missing link to high iron solubility in aerosols, Sci. Adv., 5, eaau7671, https://doi.org/10.1126/sciadv.aau7671, 2019
  34. Scanza et al., 2018 Atmos. Chem. Phys., 18, 14175–14196.
  35. Myriokefalitakis, S., et al.: Reviews and syntheses: the GESAMP atmospheric iron deposition model intercomparison study, Biogeosciences, 15(21), 6659–6684, doi:10.5194/bg-15-6659-2018, 2018.
  36. Ito, A., et al: Pyrogenic iron: The missing link to high iron solubility in aerosols, Sci. Adv., 5, eaau7671, https://doi.org/10.1126/sciadv.aau7671, 2019
  37. Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8(8), 2465–2513, doi:10.5194/gmd-8-2465-2015, 2015
  38. Seland, Ø.,  et al.: 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.
  39. Guieu, C., Dulac, F., Ridame, C. and Pondaven, P.: Introduction to project DUNE, a DUst experiment in a low Nutrient, low chlorophyll Ecosystem, Biogeosciences, 11(2), 425–442, doi:10.5194/bg-11-425-2014, 2014.
  40. Kok, J. F., et al: Contribution of the world's main dust source regions to the global cycle of desert dust, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-4, in review, 2021.
  41. Does, M. van der, Knippertz, P., Zschenderlein, P., Harrison, R. G. and Stuut, J.-B. W.: The mysterious long-range transport of giant mineral dust particles, Science Advances, 4(12), eaau2768, doi:10.1126/sciadv.aau2768, 2018.
  42. Ridley, D. A., C. L. Heald, and J. M. Prospero. “What Controls the Recent Changes in African Mineral Dust Aerosol Across the Atlantic?” Atmospheric Chemistry and Physics 14, no. 11 (2014): 5735–5747.
  43. D. A. Ridley  C. L. Heald  B. Ford: North African dust export and deposition: A satellite and model perspective, Volume117, IssueD2,  https://doi.org/10.1029/2011JD016794, 2012
  44. Yu, H., et al.: Estimates of African Dust Deposition Along the Trans-Atlantic Transit Using the Decadelong Record of Aerosol Measurements from CALIOP, MODIS, MISR, and IASI, Journal of Geophysical Research: Atmospheres, 124(14), 7975–7996, https://doi.org/10.1029/2019JD030574, 2019.
  45. Inness, A., et al.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
  46. Van der Does et al., 2016; www.atmos-chem-phys.net/16/13697/2016/
  47. Korte et al., 2017 www.atmos-chem-phys.net/17/6023/2017/
  48. van der Does et al., Sci. Adv. 2018;4 : eaau2768.
  49. https://doi.org/10.5194/bg-14-4577-2017.
  50. https://doi.org/10.1016/j.pocean.2019.102140.
  51. Chai, F., Johnson, K.S., Claustre, H., Xing, X., Wang, Y., Boss, E., Riser, S., Fennel, K., Schofield, O., Sutton, A., 2020. Monitoring ocean biogeochemistry with autonomous platforms. Nature Reviews Earth & Environment 1–12. https://doi.org/10.1038/s43017-020-0053-y
  52. Chai, F., Wang, Y., Xing, X., Yan, Y., Xue, H., Wells, M., and Boss, E.: A Limited Effect of Sub-Tropical Typhoons on Phytoplankton Dynamics, Biogeosciences Discuss, https://doi.org/10.5194/bg-2020-310, accepted, Jan 2021.

Investigators: 
Funders: 
European Space Agency