Measurements were conducted aboard the R/V Tara during the first year of the Tara Pacific expedition 12–14. The R/V Tara is a 36 m long, 10 m wide aluminum hull schooner with two 27 m long masts, equipped with a meteorological station (Station Bathos II, Météo France) measuring air temperature, relative humidity, and pressure. The station is located on the stern around 7 m above sea level, the wind speed and direction are measured at the top of the mast, ~27m above sea level (asl), and a thermo-salinometer (Sea-Bird Electronics SBE45 MicroTSG) measures sea surface temperature (SST) and salinity with its main water entrance located about 0.5-3m under the sea surface (depending on ocean conditions). The intensity of Photosynthetically Active Radiation (PAR; wavelengths between 400 and 700 nm) was measured next to the meteorological station by a QCR-2150 (Biospherical Instruments Inc.). The meteorological station recorded frequencies are listed in Table S2. The SST and salinity were measured at 0.1 Hz and processed to 1 min averages. The PAR is analyzed to 1 min average of 1 Hz measurements.
Continuous aerosol instrumentation and inlet
A detailed description of the aerosol instrumentation during the expedition can be found in Flores et al. 2020 14. In short, an optical particle counter (OPC; EDM-180 GRIMM Aerosol Technik Ainring GmbH & Co. KG, Ainring, Germany), for continuous aerosol size distribution measurements (from 0.25 – 32 µm, sorted into 31 bins), and a custom-made aerosol filter system consisting of four 47mm filter holders and one vacuum pump (Diaphragm pump ME 16 NT, VACUUBRAND BmbH & Co KG, Wertheim, Germany) were installed aboard R/V Tara. Two separate inlets, located next to each other, were constructed out of conductive tubing of 1.9 cm inner diameter and a funnel (allowing the collection of all diameters) and mounted on the rear backstay of Tara. For the Atlantic Ocean measurements, from Lorient, France to Miami, U.S.A., the inlet was installed half way up the backstay (~15m asl) and after Miami, the inlet was relocated to the top of the backstay (~27m asl).
The OPC measures single particles at 683 nm and it was calibrated at the refractive index of polystyrene latex spheres. It collects the scattered light using a wide-angle collector optic at a mean scattering angle of 90°; the optical design smoothes out Mie scattering resonances and reduces the sensitivity to particle shape. A Nafion dryer was installed before the OPC, which reduced the sampled air relative humidity to below 40 %14. The flow through the OPC was 1.2 liters per minute and it produced a particle size distribution every 60 seconds.
The filters from the custom-made system were changed, in general, twice a day, collecting aerosols for periods of at least 12 hours. The filter holder for the analysis presented here contained 0.8 µm polycarbonate filters (ATTP04700, Millipore) that were stored at room temperature in PetriSlide dishes preloaded with absorbent pads (Millipore, PDMA04700) to keep the filters dry while stored. The flow through the filter was 30 litter per minute for the filters analyzed here.
Continuous water measurements
The R/V Tara was equipped with an ocean surface flow-through autonomous sampling system, similar to the one installed during the Tara Oceans Expeditions, to measure sea surface physical and bio-optical properties as described in 15. The inline system consisted of a Sea-Bird Electronics SBE45 MicroTSG for measurements of sea surface temperature (SST) and salinity and an AC-S spectrophotometer (WET Labs, Inc.) measuring hyperspectral particulate absorption (ap) and particulate attenuation (cp) with a ~4 nm resolution, and an ECO-BB3 (WetLabs Inc.) set in a BB-box of ~4.5 L measuring particulate backscattering at three wavelength (470 nm, 532 nm and 650 nm), altogether mounted in an autonomous setup described in Dall’Olmo et al. 42 and Slade et al. 26. The size range of the measured particles is > 0.2 µm, but contribution of particles > 20 µm is assumed negligible. Particulate Organic Carbon (POC) concentrations were computed from cp 43 and chlorophyll-a concentrations were estimated from the particulate absorption line height 15. Additionally, a particle size index (γ), an estimate of the mean particle size in the ocean near-surface waters, was calculated using the wavelength-dependency of cp and that its spectral shape can be approximated as a power law (see main text).
Air mass back trajectory analysis
The presented 48 hours back trajectories in Fig. 1 were calculated using the NOAA’s HYSPLIT atmospheric transport and dispersion model 16,17. They represent the average trajectories of the ‘Ensemble option’ that were calculated based on an endpoint at 250 m height. We chose the ‘Ensemble option’ to have a better representation of where the air masses were coming from. We did not use a lower starting height as the minimum height for the optimal configuration of the ensemble is 250 m.
Diameter determination of the diurnal cycle
To obtain an estimation for the minimal diameter impacted by the changes in the diurnal emission, we analyzed each bin from the OPC. We took the data only in the Pacific Ocean and when Tara was at least 100 km away from land (islands included). The OPC has 31 bins for measurements between 0.25 to 32 µm, in Fig. S1 we show the box plot analyses for 16 bins from the OPC, up to 3.0 µm. Fig. S1 shows there is no diurnal difference for the diameters below 0.58 µm, whereas, for larger diameters the cycle is clear. However, there is an exponential decrease in concentration from the smaller diameter channels to the 0.58-0.65μm channel, suggesting the diel cycle might be masked in the smaller diameters due to higher concentrations.
Definition of the diel cycle
First, all the data was converted to mean solar time (MST) using the equation:
where UTC is the Coordinated universal time, and Lon is the longitude in degrees (west < 0, east > 0). MST assumes there is no day to day variation in the UTC of solar noon at a location, and for our dataset it is a good assumption, mainly since the majority of measurements were taken along the tropics.
Each 24 h period was analyzed independently. For a day to be considered to have a diel cycle an increase in concentration had to be observed between 06:00 and 07:00 and a decrease after 17:00, with a greater NSSA_0.58µm observed during daytime (see above for the diameter determination) during daytime. For this, we divided the day into four periods, from 00:00 to 05:00 (dawn), 07:00 to 11:59 (morning), 12:00 to 17:00 (afternoon) and from 19:00 to 23:59 (night). The counts (per fixed liter volume) measured by the particle counter can be assumed to follow a Poisson distribution, therefore their standard deviation is , where µ is the mean. Hence, for a day to be considered to have a diel cycle the two following conditions had to be met:
µmorning > µdawn+ σdawn and µnight < µafternoon – σafternoon
Figure S2 shows the places where the diel cycle was detected using this definition.
Chlorophyll-a along Tara’s route
The chlorophyll-a concentration along Tara’s route was calculated using the AC-S15 and to approximate the chl-a concentrations when the AC-S was not functioning, we used the level 3 SNPP-VIIRS satellite monthly data maps. For each month, we used Tara’s hourly location to first extract a 0.2 x 0.2 degree area for each point, then this area was averaged to get a corresponding chl-a concentration at each point. Finally, a 24-hour average was taken along Tara’s route. Figure 2B shows the satellite calculated chl-a concentration, and the in situ chl-a inferred from AC-S measurements.
Daytime and nighttime SSA0.58μm concentration vs Wind speed
In order to understand the role of wind speed in the NSSA_0.58µm cycle, we separated the Pacific data (for days when a cycle was detected) into daytime (07:00 – 17:00) and nighttime (19:00 – 05:00) periods, and binned the total aerosol counts of D > 0.58µm into 2 m s-1 bins (Fig. 3a; data within 100 km from continental coasts and Japan was not used to avoid pollution artifacts). There were between 3612 to 23136 events per bin used. The Atlantic Ocean data was binned into 4 m s-1 bins for comparison. There were between 949 to 5056 events per bin used.
Rate of change of γ (∂γ/∂t)
As mentioned above, γ is an indicator of the size distribution among particles (< 20µm in diameter) in the ocean surface. From Fig. 1 we see γ decrease at daytime (i.e. the sizes of the plankton increase) and increase over nighttime. To quantify the intensity and timing of this change over a full day, we calculated the rate of change of γ. First, to fill in data gaps that correspond to periods when the AC-S was measuring filtered seawater for calibration purposes (normally shorter than 30 minutes), we did a linear interpolation. Data gaps larger than 30 minutes were not interpolated. Then, each continuous segment was smoothed applying a low-pass digital filter with a pass band frequency of 18 hours. Then the rate of change ∂γ/∂t (hr-1) was calculated. Finally, Fig. 4B shows a box plot analysis of the days where a NSSA_0.58µm diel cycle was found and there was at least 23 hours of the AC-S data.
Diurnal cycle near Niue Island when Tara was anchored
Similar to Fig. 1 in the main text, a diel cycle of NSSA_0.58µm was detected while Tara was anchored near Niue Island (19°03′14″S 169°55′12″W). Figure S3 shows a diurnal cycle of γ, increasing during nighttime (smaller particle mean diameter) and decreasing during daytime (bigger particle mean diameter).
Box plot analysis for three different legs
Similar to the analysis shown in Fig. 3, we performed box plot analysis for the days the cycle was not detected in the Pacific Ocean (Fig. S4A), for the Atlantic Ocean transect (Fig. S4B), for the tour around Japan (Fig. S4C), and for the Fiji – New Zealand leg (Fig. S4D). In the Atlantic Ocean average counts of 4518 (±776) L-1 in the daytime and 4553 (±357) L-1 at nighttime were measured. For the Fiji – New Zealand leg average counts of 1388 (±352) L-1 during daytime and 1352 (±85) L-1 at nighttime were measured.
Day to nighttime ratio vs. aerosol concentration
To explore the relationship between the diel cycles and the aerosol concentration, we quantified the day to nighttime concentration ratio for D > 0.58 μm vs. the total (using all the bins from the OPC) nighttime aerosol concentration. For this purpose, after converting the data to mean solar time and taking every 24-hour period as independent, we first averaged the total nighttime concentration (from 19:00 to 05:00), next we took a 5-day running average, and finally the data was binned into equally number bins from low to high concentration. A 5-day running average was also taken for the day to nighttime ratio. Figure S4 shows the inverse relationship between the day to nighttime concentration ratio and the aerosol loading. The analysis was also done using the daytime and a 24hr concentration average, no significant difference was found.
Scanning electron microscope with Energy disperse X-ray analysis
Using Scanning Electron Microscopy with energy-disperse X-ray analysis (SEM-EDX) and a similar particle classification scheme as described in Laskin et al. (2012)18, we classified each particle into one of five major classes of aerosols: i) Sea salt: [Na] greater than all other elements detected (except Cl); ii) Metals with Na: [Na] present but [Na] < [Al, Si, K, Ca, S]; iii) Sulfate/SeaSalt: [Na] > [Al, Si, K, Ca] but [Na] < [S]; iv) Sulfates: [Na]=0 and [S]>0; and v) Other: all remaining particles.
To perform the SEM-EDS analysis, we used a Zeiss Sigma500 SEM with a Bruker XFlash®-6|60 Quantax EDS detector, and the Bruker ESPRIT feature software package for automatic particle detection and chemical classification in EDS.
The SEM was set at a working distance of about 7.5mm (±0.1), an accelerating voltage of 8.0kV, an aperture size of 60μm, and a magnification of 2000. The backscatter detector was used to acquire the images. For each filter four images, covering a total of 2471 μm2 surface area, were taken and each particle above a minimum area of 0.08 μm2 was counted and an EDS spectrum acquired. After the acquisition of the images and EDS spectra, we took only the particles that had an average diameter greater than 0.58 μm and for each of their corresponding EDS spectra, the method described in 44 was used to calculate the mass percent of each detected element. We excluded C from the mass percent calculation since the filters were made of polycarbonate. Following the mass calculation, particles containing sodium above 0.01 mass percent ([Na] > 0) were first separated from those without sodium. The Na containing particles with more sodium than any other detected element (besides Cl) were denoted “Sea-salt”. The rest of Na containing particles were subdivided into two classes: “Metals with Na” if [Na] < [Al], [Ca], [K], [Si], and mixed “SeaSalt/Sulfate” – if [Na] > [Al], [Ca], [K], [Si] [Na] but [Na] < [S]. The sodium-free particles were assigned to two classes: “Sulfate” if [S] > 0 and “Other” for the remaining particles. A total of 14339 particles, where 8266 had average D ≥ 0.58 μm, were analyzed. In the 14 daytime filters (for the period between 4 May and 17 May, 2017; see Table S1) we counted a total of 7247 particles and 4560 with D ≥ 0.58 μm. In the 15 nighttime ones we counted a total of 5894 particles and 3706 with D ≥ 0.58 μm. We had between 80 to 781 particles per filter.
Figure S5A shows the SSA0.58μm counts per litter calculated using the SEM images (particle count and area imaged) and the total air sampled. Figure 5SB shows histograms of the chlorine mass percentage found in the particles per filter. Between May 4 and May 9 we see a noticeable Cl depletion, suggesting the atmospheric marine boundary layer in this region had anthropogenic pollutants.
Atmospheric Diurnal anomalies
We calculated diurnal anomalies for the air temperature, relative humidity (RH7m), and wind speed (U27m), in the Pacific Ocean for two scenarios: 1) the days where a diel cycle in NSSA_0.58µm was detected and 2) when there was no diel cycle in NSSA_0.58µm (Fig. S6). The average between midnight and 05:00 is the baseline for each variable. To avoid continental influence, this analysis was done only in the open ocean and near the Pacific islands except Japan and Fiji.
The air temperature and relative humidity anomalies show no discernible differences between days where a cycle was detected (Fig S6; panels AirT_a, RH_a) and when there was no cycle (FigS6; panels AirT_b, RH_b). This implies that even though both atmospheric variables have a diurnal signature, their changes, especially in relative humidity, cannot explain the diurnal patterns seen for NSSA_0.58µm.
Finally, the wind speed anomaly analysis (Fig. S6, WS_a, WS_b) also does not show sharp changes at sunrise or sunset; there might be isolated cases, but no consistent pattern.
Effect of atmospheric variables on the SSA0.58μm diurnal cycle
Rain, relative humidity, air temperature, and atmospheric instability can have an effect on the production, growth, transport and removal of SSA. Here we explain why these variables do not explain the NSSA_0.58µm diel cycle.
First, rain suppressed the NSSA_0.58µm cycle (Fig. 1A and Fig. S5A), as it is a known washout mechanism of aerosols. Second, there are several indications against RH as a major driving factor underlying the detected diurnal cycle in SSA0.58µm concentration. For example, for a given SSA, its dry diameter is around ¼ of its diameter at formation (1). Hence, if RH variations were the cause, we expect to see the diurnal patterns in all sizes, and especially at smaller diameters, but this is not the case (Fig. S1). Additionally, the NSSA_0.58µm diel cycles were observed in days with and without daily variations in air temperature and RH (Fig. S7). In addition, RH and air temperature diurnal anomalies have similar trends for days when the NSSA_0.58µm diel cycles was observed and when it was not (Fig. S6). Finally, the atmospheric stability that influences the transport of aerosols from the ocean surface upward does not explain the diurnal cycle either. Firstly, under most atmospheric conditions, concentrations of SSA with Ddry < 10 µm are well mixed in the marine boundary layer, showing little variation with height (1), hence a change in stability conditions will most likely not cause a change in NSSA_0.58µm. In addition, during the Taiwan – Fiji transect, the cycle appeared in three distinct atmospheric states: with clear skies at low wind speeds, with overcast conditions and with trade cumulus throughout the day (see Fig. S8). Especially, that a cycle is observed even if the morning is overcast (Fig. S8b), and that there are cycles when there is no air temperature variability (Fig. S7), infers that most likely atmospheric stability does not play a significant role in the observed diel cycles. Therefore, we conclude that the atmospheric variables cannot explain the observed NSSA_0.58µm diel cycle.
Photosynthetically active radiation (PAR) vs daytime to nighttime ratio of concentration
By definition, solar radiation drives diurnal cycles. Therefore, we explored links between the intensity of solar radiation, measured by the average daytime photosynthetically available radiation (PAR), and the average daytime number count of SSA0.58µm to determine if the intensity of solar radiation has a measurable effect on the total amount of SSA0.58µm. We considered the nighttime (background) concentration by calculating the ratio of daytime to nighttime number concentration for the same day and plotted it against the average PAR (Fig. S9). No clear correlation between PAR and NSSA_0.58µm was found. Furthermore, examples of days with similar PAR that showed different daytime NSSA_0.58µm can be seen in Fig. 1B and Fig. S8. This analysis suggests the NSSA_0.58µm diel cycle is not caused directly by changes in solar radiation, but that there is a parallel mechanism.
Surface Ocean variables anomalies
Similar to the atmospheric variables anomalies, we calculated the rate of change (∂POC/∂t) for particulate organic carbon (POC) and diurnal anomalies for chlorophyll a, salinity, and SST for days where the cycle was detected and not in the Pacific Ocean (Fig. S10). The average between midnight and 05:00 is the baseline for each variable. To avoid continental influence, this analysis was done only in the open ocean and near the Pacific islands except Japan and Fiji.
The Chl a and salinity anomalies analysis does not show any diurnal changes. The POC shows a decrease during nighttime and an increase during daytime both when we detected a NSSA_0.58µm diel cycle and when we did not. The daytime increase in POC can be attributed to photosynthetic growth or particle aggregation. Similarly, in the SST anomaly, for both cases, when cycles were detected (Fig. S10, SST_a) and not detected (Fig. S10, SST_b), we see a diurnal signature. However, here we see no difference from the base line up to 09:00, and only a gradual increase from 10:00. Depending on wind conditions, there can be a few degrees difference between SST at the skin of the ocean surface and SST at 0.5-3m depth, and expect to have a stronger diurnal cycle near the skin of the ocean (18), but we don’t expect to have a sharp change at sunrise or sunset.
Particle size index γ in different parts of the ocean
To understand the differences in γ and how it might be related to SSA0.58µm production, we calculated the average γ in 24 hour cycles. Figure S11 shows three different scenarios: Fig. S11A shows the average γ measured in the Atlantic Ocean and in the Pacific Ocean when diurnal cycles in NSSA_0.58µm were detected. Fig. S11B shows the average γ measured during the Keelung – Fiji leg separated by different latitude ranges and the average γ while Tara was anchored near Niue Island. Finally, Fig. S11C shows the average γ measured in the transect from Fiji to New Zealand. This shows the latitudinal dependence of γ at low latitudes and that in the Atlantic Ocean there is a larger mean particle size, which suggest the presence of larger planktonic species than in the Pacific Ocean.
Contribution of small, ~<1 µm particles, to γ variations
In order to estimate what are the daytime γ changes associated with, we calculated the contribution of small particles to the size changes observed in γ. To do so, we calculated the backscattering (bbp) to total particulate scattering (bp) ratio (bbp:bp) at λ = 532 nm.