It is well recognized that the atmospheric deposition of iron (Fe) affects ocean productivity, atmospheric CO 2 uptake, ecosystem diversity, and overall climate. Despite significant advances in measurement techniques and modeling efforts, discrepancies persist between observations and models that hinder accurate predictions of processes and their global effects. Here, we provide an assessment report on where the current state of knowledge is and where future research emphasis would have the highest impact in furthering the field of Fe atmosphere-ocean biogeochemical cycle. These results were determined through consensus reached by diverse researchers from the oceanographic and atmospheric science communities with backgrounds in laboratory and in situ measurements, modeling, and remote sensing. We discuss i) novel measurement methodologies and instrumentation that allow detection and speciation of different forms and oxidation states of Fe in deliquesced mineral aerosol, cloud/rainwater, and seawater; ii) oceanic models that treat Fe cycling with several external sources and sinks, dissolved, colloidal, particulate, inorganic, and organic ligand-complexed forms of Fe, as well as Fe in detritus and phytoplankton; and iii) atmospheric models that consider natural and anthropogenic sources of Fe, mobilization of Fe in mineral aerosols due to the dissolution of Fe-oxides and Fe-substituted aluminosilicates through proton-promoted, organic ligand-promoted, and photo-reductive mechanisms. In addition, the study identifies existing challenges and disconnects (both fundamental and methodological) such as i) inconsistencies in Fe nomenclature and the definition of bioavailable Fe between oceanic and atmospheric disciplines, and ii) the lack of characterization of the processes controlling Fe speciation and residence time at the atmosphere-ocean interface. Such challenges are undoubtedly caused by extremely low concentrations, short lifetime, and the myriad of physical, (photo)chemical, and biological processes affecting global biogeochemical cycling of Fe. However, we also argue that the historical division (separate treatment of Fe biogeochemistry in oceanic and atmospheric disciplines) and the classical funding structures (that often create obstacles for transdisciplinary collaboration) are also hampering the advancement of knowledge in the field. Finally, the study

Abstract. A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters >50 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations (<0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13 % and −22 % for updraft velocities 0.3 and 0.6 m s−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

A key Earth system science question is the role of atmospheric deposition in supplying vital nutrients to the phytoplankton that form the base of marine food webs. Industrial and vehicular pollution, wildfires, volcanoes, biogenic debris, and desert dust all carry nutrients within their plumes throughout the globe. In remote ocean ecosystems, aerosol deposition represents an essential new source of nutrients for primary production. The large spatiotemporal variability in aerosols from myriad sources combined with the differential responses of marine biota to changing fluxes makes it crucially important to understand where, when, and how much nutrients from the atmosphere enter marine ecosystems. This review brings together existing literature, experimental evidence of impacts, and new atmospheric nutrient observations that can be compared with atmospheric and ocean biogeochemistry modeling. We evaluate the contribution and spatiotemporal variability of nutrient-bearing aerosols from desert dust, wildfire, volcanic, and anthropogenic sources, including the organic component, deposition fluxes, and oceanic impacts.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; A total of sixteen global chemistry transport models and general circulation models have participated in this study. Fourteen models have been evaluated with regard to their ability to reproduce near-surface observed number concentration of aerosol particle and cloud condensation nuclei (CCN), and derived cloud droplet number concentration (CDNC). Model results for the period 2011&amp;#8211;2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations, located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments, and on the seasonal and short-term variability in the aerosol properties.&lt;/p&gt; &lt;p&gt;There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of &amp;#8722;24&amp;#8201;% and &amp;#8722;35&amp;#8201;% for particles with dry diameters &gt;&amp;#8201;50&amp;#8201;nm and &gt;&amp;#8201;120&amp;#8201;nm and &amp;#8722;36&amp;#8201;% and &amp;#8722;34&amp;#8201;% for CCN at supersaturations of 0.2&amp;#8201;% and 1.0&amp;#8201;%, respectively. Fifteen models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3&amp;#8201;nm) and up to about 1 for simulated CCN in the extra-polar regions.&lt;/p&gt; &lt;p&gt;An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter.&lt;/p&gt; &lt;p&gt;Models capture the relative amplitude of seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120&amp;#8201;nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40&amp;#8201;% during winter and 20&amp;#8201;% in summer.&lt;/p&gt; &lt;p&gt;In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB &amp;#8722;17&amp;#8201;% and &amp;#8722;22&amp;#8201;% for updraft velocities 0.3 and 0.6&amp;#8201;m&amp;#8201;s&lt;sup&gt;&amp;#8722;1&lt;/sup&gt;, respectively). In addition, simulated CDNC is in slightly better agreement with observationally-derived value at lower than at higher updraft velocities (index-of-agreement of 0.47 vs 0.50). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration and to updraft velocity. Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high.&lt;/p&gt;

&lt;p&gt;Anthropogenic emissions of nitrogen and sulphur oxides and ammonia have altered the pH of aerosol, cloud water and precipitation, with significant decreases over much of the marine atmosphere. Some of these emissions have led to an increased atmospheric burden of reactive nitrogen and its deposition to ocean ecosystems. Changes in acidity in the atmosphere also have indirect effects on the supply of labile nutrients to the ocean. For nitrogen, these changes are caused by shifts in the chemical speciation of both oxidized (NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;-&lt;/sup&gt; and HNO&lt;sub&gt;3&lt;/sub&gt;) and reduced (NH&lt;sub&gt;3&lt;/sub&gt; and NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;) forms that result in altered partitioning between the gas and particulate phases that affect transport. Other important nutrients, notably iron and phosphorus, are impacted because their soluble fractions increase due to exposure to low pH environments during atmospheric transport. These changes affect not only the magnitude and distribution of individual nutrient supply to the ocean but also the ratios of nitrogen, phosphorus, iron and other trace metals in atmospheric deposition. &amp;#160;Since marine microbial populations are sensitive to nutrient supply ratio, the consequences of atmospheric acidity change include shifts in ecosystem composition in addition to overall changes in marine productivity. Nitrogen and sulphur oxide emissions are decreasing in many regions, but ammonia emissions are much harder to control. The acidity of the atmosphere is therefore expected to decrease in the future, with further implications for nutrient supply to the ocean.&lt;/p&gt;&lt;p&gt;This presentation will explore the impact of increased atmospheric acidity since the Industrial Revolution, and the projected acidity decreases, on atmospheric nutrient supply and its consequences for the biogeochemistry of the ocean.&lt;/p&gt;