Dimethylsulfide (DMS) emitted from the ocean makes a
significant global contribution to natural marine aerosol and cloud
condensation nuclei and, therefore, our planet's climate. Oceanic DMS
concentrations show large spatiotemporal variability, but observations are
sparse, so products describing global DMS distribution rely on interpolation or modelling. Understanding the mechanisms driving DMS variability, especially at local scales, is required to reduce uncertainty in large-scale DMS estimates. We present a study of mesoscale and submesoscale (< 100 km) seawater DMS variability that takes advantage of the recent expansion in high-frequency seawater DMS observations and uses all available data to investigate the typical distances over which DMS varies in all major ocean basins. These DMS spatial variability length scales (VLSs) are uncorrelated with DMS concentrations. The DMS concentrations and VLSs can therefore be used separately to help identify mechanisms underpinning DMS variability. When data are grouped by sampling campaigns, almost 80 % of the DMS VLS can be explained using the VLSs of sea surface height anomalies, density, and chlorophyll a. Our global analysis suggests that both physical and biogeochemical processes play an equally important role in controlling DMS variability, which is in contrast with previous results based on data from the low to mid-latitudes. The explanatory power of sea surface height anomalies indicates the importance of mesoscale eddies in driving DMS variability, previously unrecognised at a global scale and in agreement with recent regional studies. DMS VLS differs regionally, including surprisingly high-frequency variability in low-latitude waters. Our results independently confirm that relationships used in the literature to parameterise DMS at large scales appear to be considering the right variables. However, regional DMS VLS contrasts highlight that important driving mechanisms remain elusive. The role of submesoscale features should be resolved or accounted for in DMS process models and parameterisations. Future attempts to map DMS distributions should consider the length scale of variability.
Natural Environment Research CouncilNE/R007586/1NE/W009277/1Horizon 2020101003536ERC-2018-ADG-834162Spanish National Plan for Scientific and Technical Research and InnovationCTM2016-81008-R202230I123CEX2019-000928-SIndian Institute of Tropical Meteorology(IITM)Met Officen/aIntroduction
Dimethylsulfide (DMS) is a volatile sulfur gas produced by surface ocean
microbial food webs and emitted to the atmosphere (Bates et al., 1992). DMS emissions dominate atmospheric biogenic sulfur and form a significant
component of natural marine aerosol loads (Simó, 2001; Sanchez et al.,
2018; Quinn et al., 2017). Aerosols increase light scattering and modify
cloud optical properties, thereby contributing to a radiative forcing of
climate (Charlson et al., 1987; Carslaw et al., 2013; Galí et al.,
2021). The amount, composition, and distribution of natural aerosol in the
atmosphere determine the indirect radiative forcing effect of anthropogenic
aerosols on climate, but these are poorly constrained by global climate models (Carslaw et al., 2013). DMS-derived sulfate aerosols are ephemeral
(∼ 1 d residence time; Boucher et al., 2003) and of greater
consequence for cloud modulation in remote pristine regions (Halloran et
al., 2010). The distribution of natural marine aerosol sources should be
represented at the resolution required to capture the frequency and
magnitude of their variability. This is critical for reducing the large
uncertainties associated with natural aerosol–cloud interactions.
Oceanic DMS production and consumption pathways are complex, and the
controls on DMS spatial distribution in the global ocean are not fully
resolved (Galí and Simó, 2015). The Global Surface Seawater DMS
Database (GSSDD) contains measurements that show large-scale temporal and spatial
variability in DMS concentrations (Lana et al., 2011; Hulswar et al., 2022).
In situ DMS measurements are relatively sparse and limited with respect to global distribution, coverage, and spatiotemporal sampling frequency, rendering the majority of DMS observations insufficient to resolve local and submesoscale variability (Tortell et al., 2011; Belviso et al., 2004a; Lana et al., 2011). DMS sampling is globally biased towards spring–summer months
(see Fig. S1, Supplement) and has disproportionally targeted
biologically productive areas (e.g. northeastern Pacific and northwestern
Atlantic, see Fig. 1), which can lead to an overrepresentation of high DMS
concentrations within the database (Galí et al., 2018). Monthly and
repeat interannual DMS measurements are rare and generally restricted to
DMS productive areas (Galí et al., 2018; Tesdal et al., 2015). Sparse,
infrequent, and seasonally and spatially biased observations of highly variable
DMS concentrations create uncertainty because it is hard to quantify the
representativeness of the measurements. Sampling uncertainties inevitably
propagate through to DMS concentration and flux climatologies,
parameterisations, and model outputs (Belviso et al., 2004b).
Global extent of the 37 high-frequency DMS campaigns included in
this analysis (coloured). Data are only shown for the underway transects
used in the VLS analysis (see Sect. 2.3). Insets show detail for northeastern
Pacific and northwestern Atlantic regions with multiple sampling campaigns (see Table S2 and Fig. S1 in the Supplement for metadata relating to
each sampling campaign and the spatiotemporal distribution, respectively).
Relatively simple extrapolation methods have been used to fill the gaps
between sparse observations to provide globally representative estimates of
DMS (Lana et al., 2011; Kettle et al., 1999; Hulswar et al., 2022).
Significant differences in these smoothed climatological estimates, and thus
uncertainties, have been attributed to the gap-filling techniques used,
specifically the appropriate interpolation/smoothing radius of influence
(Hulswar et al., 2022). More complex algorithms have been generated at the
basin or global scale using parameters such as chlorophyll, light,
nutrients, surface temperature, and mixed layer depth (Simó and Dachs,
2002; Anderson et al., 2001; Aranami and Tsunogai, 2004; Halloran et al.,
2010; Vallina and Simó, 2007; Aumont et al., 2002; Belviso et al.,
2004a; Galí et al., 2015, 2018; Miles et al., 2009; Chu et al., 2003;
Herr et al., 2019). More recently, global and regional climatologies have
been generated using machine learning approaches (Wang et al., 2020; McNabb
and Tortell, 2023, 2022; Humphries et al., 2012). The variation in different
climatological DMS estimates highlights that the scientific community needs
to better understand and map the processes controlling its oceanic
distribution (Halloran et al., 2010; Belviso et al., 2004b). Modelled
seasonal and regional aerosol–cloud interactions and radiative forcing are
directly sensitive to the accuracy and choice of seawater DMS estimates
(Woodhouse et al., 2013, 2010; Mahajan et al., 2015).
Recent studies have focussed on local and submesoscale DMS variability,
taking advantage of improvements to seawater DMS concentration sampling
resolution (e.g. Asher et al., 2011; Nemcek et al., 2008; Tortell, 2005a,
b; Tortell and Long, 2009; Zindler et al., 2014). This study explores
the potential mechanisms that appear to govern DMS variability at the
< 100 km scale and investigates whether these align with the
variables used within large-scale DMS parameterisations. An improved
understanding of submesoscale DMS variability will aid the development of
future climatological flux estimates and the appropriate radius of influence
that sparse observations should be afforded when smoothing and interpolating
in situ observations.
Variability length scale (VLS) analysis is a powerful tool for quantifying
submesoscale variability. VLS analysis can be used to indicate the lowest sampling resolution necessary to capture most of the spatial variability
(Royer et al., 2015). High-resolution measurements are required to assess
small-scale variability. For example, observing variations within 10 km
when the research ship is travelling at 8 m s-1 requires measurements
every 20 min. Instruments that can observe variability at these high
resolutions have been deployed in recent years and have substantially contributed to the global DMS database (Hulswar et al., 2022). A growing
number of high-frequency DMS measurements offers the opportunity for a
global analysis of the drivers of DMS variability at small scales.
VLS analysis for DMS has been applied in only a few studies, with most
focusing on a specific region and/or a single sampling campaign (e.g. Ross
Sea, Tortell and Long, 2009; Tortell et al., 2011; northeastern subarctic
Pacific, Tortell, 2005b; Asher et al., 2011; Nemcek et al., 2008). A larger-scale VLS analysis was undertaken on the 7-month low- to mid-latitude global circumnavigation conducted during the Malaspina Expedition 2010 (M10; Royer et
al., 2015). Royer et al. (2015) combined their VLS analysis with VLS values
from three high-latitude studies (7–15 km; Asher et al., 2011; Nemcek et al.,
2008; Tortell et al., 2011) and reported an inverse relationship between DMS
VLS and latitude (R=-0.74, p< 0.005). Royer et al. (2015) also reported that biological variables dominate over physical variables as
drivers of DMS VLS in low-latitude regions. While it is tempting to draw
global conclusions from the similarities and differences between these
studies, each study adopts a slightly different approach to the data
treatment, measurement of interpolation error, and/or classification of VLS
(see Table S1, Supplement).
This study applies a single, objective VLS analysis to high-frequency global
DMS observations over the past 15 years (Figs. 1 and S1, Supplement). The dataset used
includes all available data from previous VLS studies. Our study assesses
whether the factors controlling DMS variability can be identified using a
submesoscale variability analysis across all ocean basins. Section 2
describes the datasets used and the VLS methodology. Section 3 presents
results including global VLS statistics, regional patterns of DMS
variability, and drivers of DMS variability. Finally, the findings are
discussed in Sect. 4, with conclusions made in Sect. 5.
Data and methodsSeawater DMS data
The majority of DMS data are sourced from the global surface seawater DMS
database (GSSDD; see https://saga.pmel.noaa.gov/dms/, last access: 15 April 2022).
Selection criteria are used to identify datasets suitable for submesoscale
VLS analysis: a minimum of 100 data points in total and ≤ 1 h between
measurements, which excludes all data with a spatial resolution of > 30 km. Applying these filters results in 37 eligible datasets (collected between 2004 and 2019). The filters broadly separate the DMS database by sampling method, highlighting the rapid shift during the early 2000s from discrete, low-frequency gas chromatography analytical systems to
continuous, semi-automated high-frequency mass spectrometry (Bell et al.,
2012; Saltzman et al., 2009). Additional data are from the Malaspina
Expedition in 2010–2011 (M10; Royer et al., 2015), the North Atlantic
Aerosol and Marine Ecosystem Study in 2015–2018 (NAAMES; Bell et al., 2021;
Figs. 1 and S1, Table S2, Supplement, campaign numbers: 33 – blue, 34 – green, 35 – red, 36 – yellow), and the Southern oCean SeAsonaL Experiment in 2019 (SCALE; Manville and Bell, 2023; Figs. 1 and S1, Table S2, Supplement, campaign number: 37, green). The M10 circumnavigation data are split spatiotemporally into three datasets, each broadly covering different ocean basins (Figs. 1 and S1, Table S2, Supplement, campaign numbers: 30 – M10a, black; 31 – M10b, dark red; 32 – M10c, cyan).
Ancillary in situ and coincident satellite measurements
Ancillary in situ and remotely sensed data are used to explore the potential
mechanisms driving DMS variability. In situ sea surface salinity (hereafter
salinity) and sea surface temperature (SST) from each DMS dataset are used to derive sea surface density (hereafter density) (see Fernandes, 2014).
Satellite monthly mean chlorophyll a (Chl) and 5 d sea surface height
anomaly (SSHA) data are matched to the average date of each DMS sampling
cruise. Satellite data pixels are extracted along the coordinates of the DMS
cruise track using the NASA SeaDAS software (version 7.5.3). NASA MEaSUREs level 4 (L4) 0.17∘ 5 d SSHA data are used to explore the role of eddies in driving DMS variability (Zlotnicki et al., 2019). NASA MODIS-Aqua level 3 (L3) 4 km
monthly Chl is used as a proxy for plankton biomass and biological
productivity (NASA Goddard Space Flight Center, 2018).
Data processing
Underway data are screened to only include data acquired when the ship speed was
> 1 m s-1 to avoid measurements made when ships were
sampling on station. Ship speed is calculated from distance and time between
measurements. Each DMS dataset and all its ancillary data are divided into
transects. Transects are defined as continuous data sections with a minimum
sampling frequency of 1 h. Most observations (83 %) captured by the
temporal filter are < 2.2 km apart. The minimum transect length is
calculated in two stages: (1) the linear distance between the start and end
of a continuous data section must be > 100 km to avoid campaigns
that targeted a specific area multiple times (e.g. a productive bloom or
mesoscale eddy); (2) each dataset is divided into equal length transects,
with an along-track distance of at least 100 km. The initial data processing
yields 1039 continuous transects from 37 DMS campaigns, with each transect
100–199 km in cumulative length (Fig. 1).
Variability length scale (VLS) analysis
Previous DMS VLS studies have not applied a standardised or consistent
approach (Royer et al., 2015; Asher et al., 2011; Tortell et al., 2011;
Tortell, 2005b; Tortell and Long, 2009; Nemcek et al., 2008). The analysis
presented here adopts the method used to study the VLS of seawater CO2
(Hales and Takahashi, 2004), which was later applied to DMS by Tortell et
al. (2011) and Nemcek et al. (2008).
The highest observational DMS sampling resolution in the datasets is
typically between 0.2 and 2.2 km. Each data transect is subsampled
repeatedly, starting from the first data point, at increasingly coarse
spacings ranging from 2.2 km to half the length of the transect (the lowest
possible resolution), increasing in 0.2 km increments. At each subsampling
resolution, the first and last subsampled points of the data transect define
the subsampling window. Subsampled data across the subsampling window are
linearly interpolated to the resolution of the original data. Where the
subsampling window matches the length of the data transect, the
interpolation error associated with the subsampling resolution is calculated
as the root mean squared error (RMSE) between the original and the
interpolated values. Where the subsampling window is not equal to the length
of the transect, the window is shifted along the transect, incrementing by
one data point, and the transect is re-subsampled. Re-subsampled data are
linearly interpolated across the shifted window, and the RMSE is
re-calculated. The subsampling window is repeatedly shifted along the data
transect and interpolation RMSE re-calculated until the subsampling ends on the last data point of the transect. The error associated with the subsampling
resolution is taken as the average of all the RMSE values produced by
sliding the window across the data transect at that resolution. The RMSE is
calculated following Eq. (1):
RMSE=(Obs-Interp)2‾.
The RMSE typically increases in proportion to the coarseness of the subsampling until a maximum error plateau, or asymptote, is reached. The maximum error plateau corresponds to the total variance of the dataset (Tortell et al., 2011; Belviso et al., 2004a). The trend in RMSE as a function of subsampling resolution is well described by a non-linear first-order inverse exponential rise function following Eq. (2):
Ex=E∞1-e-xVLS,
where Ex is the interpolation error at subsampling resolution x,
E∞ is the asymptotic maximum interpolation error at an infinite
subsampling resolution, and VLS is the characteristic length scale of
variability. The VLS is determined by the subsampling resolution (interpolation
distance) where a tangent of the initial slope intersects with the maximum
error (E∞, Fig. 2). The VLS also corresponds to the intersect on the curve (Ex) that is 63 % of E∞, i.e. Eq. (3):
ExE∞=1-e-xVLS≈0.63.
Previous work suggested that a sudden change (or “breakpoint”) in the RMSE
slope can be used to characterise the DMS VLS (Royer et al., 2015; Asher et
al., 2011). However, this approach is unreliable because the data assessed
in this study show that the breakpoint does not always occur, and its
identification is subjective (see Table S1, Supplement).
(a) Example of seawater DMS concentration (nM) data transect (sampled from the northwestern Atlantic during the NAAMES1 (November 2015) campaign; see Bell et al., 2021), analysed to find the variability length scale (VLS). (b) Asymptotic error curve (dashed black) fitted to interpolation errors (RMSE, nM; dotted cyan) plotted as a function of increasingly coarse interpolation distance (km). The 95 % prediction intervals (PIs) of the non-linear regression fit, i.e. ±2× residual
standard errors (RSE), are shaded blue. The VLS (km) is characterised as the
intercept (dashed red) on the curve at 63 % of the asymptotically
approached maximum interpolation error (nM). Method adapted from Hales and
Takahashi (2004).
An inverse exponential rise function (Eqs. 2 and 3) is used here to
objectively derive the VLS. The objective VLS method is applied to all 1039
transects and six variables: DMS, SST, salinity, density, Chl, and SSHA.
Quality assurance and VLS statistics
Two filters are used to identify viable data transects. The VLS is rejected if
the distance is greater than the maximum subsampling/interpolation
distance (equal to half the transect length), which only occurred in very
noisy datasets. The second filter is the quality of fit to the data using
the residual standard error (RSE) (Fig. 2b), which is defined as RSE =(ssres/n), where n is the number of data
points in the transect and ssres is the sum of the squares of the residuals, i.e. ssres=∑(residuals from
fitted curve)2.
The RSE is normalised using the maximum RSE of the curve (i.e. (RSE / RSE at the asymptote) × 100), and if the normalised RSE exceeds 10 %, the
curve is deemed to inadequately describe the data and the transect is
rejected. The two quality control filters reduce the initial 1039 transects
to 763 “viable” transects.
The VLS distributions from the 763 transects are skewed for all
parameters (Figs. 3 and S2, Supplement). The geometric mean and geometric standard
deviation (GSD) are computed to assess central tendency and spread while
accounting for skew in the data. Note that the geometric mean is regularly
referred to as the “average” within this paper to aid readability. All
significance testing uses the non-parametric Mann–Whitney U test. Transects
are grouped and averaged by sampling campaign to assess underlying spatial
and temporal (regional and seasonal) patterns of variability. Average VLS
distances are calculated for each sampling campaign and for all variables
(VLSDMS, VLSSST, VLSsalinity, VLSdensity, VLSChl, VLSSSHA). A minimum threshold of four transects was necessary before
calculating a campaign-averaged VLS. Exclusion of campaigns with fewer than four transects reduced the total number of campaigns from 37 to 35.
Frequency distribution of variability length scales (VLSs, km) for
all DMS transects (grey bars). Vertical coloured lines correspond to the
global geometric mean (and geometric standard deviation, GSD) from all
transects for VLSDMS (dark blue), VLSSSHA (beige), VLSdensity (cyan), and VLSChl (magenta).
Correlation and multiple linear regression (MLR) are used to explore the
global controls on VLSDMS (see Sect. 3.3.1 and 3.3.2). The
campaign-averaged VLS used in each correlation and regression analysis only
includes transects where coincident VLS can be calculated from the DMS and
non-DMS variables. The MLR models with two input variables contain 20–26
datasets, and MLR models with three input variables contain 11–15 datasets. The relative importance of the input variables in each MLR
model are calculated based on the incremental R2 used to determine
interactional dominance (defined as the incremental R2 contribution of
each predictor to the complete model; Azen and Budescu, 2003).
ResultsGlobal VLS statistics
The global average DMS concentration and geometric standard deviation (GSD)
for the viable transects covered in this study are 2.23 nM (average) and
2.29 (GSD), which are similar to the global average and GSD from the GSSDD
(2.66 nM (average), 2.88 (GSD); https://saga.pmel.noaa.gov/dms/, last access: 15 April 2022). The
similarity between the two datasets suggests that the data used in this
study are representative of global observations. The global average and GSD
of VLSDMS from all 763 transects are 12.57 km and 2.33, respectively (Fig. 3). The global average VLSDMS is the smallest of the six variables tested, with VLSSSHA the most similar (15.76 km, 1.77 GSD) (Fig. 3). Global average VLSChl is slightly larger (20.89 km, 1.67 GSD) and similar to global average VLSdensity (20.21 km, 1.76 GSD) and its components VLSSST (21.23 km, 1.73 GSD) and VLSsalinity (19.52 km, 1.84 GSD) (Figs. 3 and S1, Supplement). Global average VLSDMS and VLSSSHA are significantly different (p< 0.01) from each other and the global average VLS of
all other parameters. Global average VLSChl, VLSdensity,
VLSSST, and VLSsalinity are not significantly different from one another.
All six variables have an average spatial variability that is tens of
kilometres in all regions. The global average VLSSSHA is similar (within 4 km) to the global average VLSDMS. The global average VLSs for all other parameters are within 9 km of the global average VLSDMS. The campaign-averaged VLSDMS ranges from 2 to 30 km, which is the same order of magnitude as the range of 7–50 km reported by other DMS variability studies (Asher et
al., 2011; Nemcek et al., 2008; Tortell, 2005b; Tortell et al., 2011; Royer
et al., 2015). Note that a detailed comparison between studies should be
treated with caution because each has used different methods to identify
the VLS.
Regional patterns of DMS variability
VLSDMS is generally small in the subtropical gyres, specifically the equatorial and subtropical South Pacific and South Atlantic (Fig. 4; see
Table S2, Supplement, for the campaign-averaged VLSDMS of each sampling campaign, e.g. campaign numbers: 30, M10a, black; 31, M10b, dark red; and 32, M10c, cyan). The average VLSDMS from all transects in the three M10 low- to mid-latitude circumnavigation campaign datasets (mean = 6.34 km, GSD = 2.59) is consistently smaller than the global value (mean = 12.57 km, GSD = 2.33)
(Fig. 4). The relative homogeneity of small VLSDMS in these
oligotrophic domains is not replicated in the VLS of any other variables
(Fig. S3, Supplement). The subtropical gyres of the Southern Hemisphere are permanently
stratified biomes, bounded to the south by a band of seasonally stratified
biomes (Fay and McKinley, 2014). At the boundary transitions from
permanently to seasonally stratified conditions, there are some notable
exceptions to the low VLSDMS, e.g. the Benguela upwelling (southeast Atlantic) and South Australia upwelling (Fig. 4).
Global distribution of 763 transects coloured by VLSDMS (km, log scale). The colour bar diverges at the global geometric mean VLSDMS (12.57 km). See Fig. S3 in the Supplement for equivalent VLS distribution maps of Chl, density, SSHA, salinity, and SST and Fig. S1 for the spatiotemporal distribution of VLSDMS.
In contrast, the average (mean = 22.06 km, GSD = 1.60) of VLSDMS in the Peruvian upwelling (eastern equatorial Pacific) is consistently larger than the global average (mean = 12.57 km, GSD = 2.33) (Fig. 4). Larger VLSDMS values are also found along parts of the Pacific and Atlantic coastlines of North America, with smaller VLSDMS values further offshore (Fig. 4, inset). The
VLSDMS values in the Arctic, northeastern Pacific, northwestern Atlantic, and
southeast Indian open ocean regions are highly variable. The Southern Ocean
has VLSDMS generally below the global average and features some
localised pockets with larger VLS (Fig. 4). The DMS concentration variability in mid- to high-latitude regions is seasonal (Hulswar et al., 2022), and
VLSDMS could be influenced by the season/time of year.
Drivers of DMS variabilityTransect and campaign-averaged VLS regressions
Simple linear regressions are used to explore the relationship between
VLSDMS and VLS for SST, salinity, density, Chl, and SSHA. The
possibility of a relationship with latitude (as discussed in Royer et al., 2015) is also investigated. Transect and campaign-average VLSDMS do not vary with latitude (R2= 0.02, n= 35, p> 0.05; Table 1). No significant relationships are observed between transect VLSDMS and VLS for SST, SSS, density, Chl, and SSHA. Averaging transect VLS data into campaign averages reduces the noise and enables statistically significant relationships to be identified. The campaign-averaged VLSdensity
explains 37 % of the variations in VLSDMS (Table 1; Fig. 5a);
VLSSSHA (used as an indicator of the dynamic eddy field in the open
ocean) and VLSChl each explain approximately half of the campaign-averaged VLSDMS (46 % and 47 %, respectively; Table 1; Fig. 5b and c).
Campaign geometric mean VLSDMS (km) plotted versus (a) VLSdensity, (b) VLSSSHA, and (c) VLSChl. Error bars indicate 1 GSD of the data within each campaign. (d) Campaign geometric mean VLSDMS predicted using coefficients from the VLSSSHA–Chl–density multiple linear regression model (Model 7, Table 1; Supplement, Table S4; Fig. S4) versus observed VLSDMS for the subset of 12 DMS campaigns included in the multiple linear regression model. The 1 : 1 line is shown.
Regression results for the prediction of campaign-averaged
VLSDMS, using different combinations of input parameters. Models are ranked in order of how much VLSDMS variance is explained. Models that are significant (p< 0.01) are denoted using ∗.
ModelInputR2Adj.pRelativeN (no. ofNo. of transects used tono.parametersR2importancecampaigns)calculate campaign(%)averages (of 760)Linear regression 1VLSChl0.47–< 0.01∗100293512VLSSSHA0.46–< 0.01∗100243613VLSdensity0.37–< 0.01∗100324804VLSsalinity0.33–< 0.01∗100324905VLSSST0.21–0.014100284456Latitude (abs.)0.02–0.37510035760Multiple linear regression 7VLSChl0.830.77< 0.01∗341287VLSSSHA52VLSdensity148VLSChl0.770.71< 0.01∗5815100VLSSSHA41VLSsalinity19VLSChl0.660.63< 0.01∗5426224VLSdensity4610VLSsalinity0.620.59< 0.01∗8525322VLSSST1511VLSChl0.510.46< 0.01∗3520177VLSSSHA6512VLSChl0.500.45< 0.01∗7022211VLSsalinity3013VLSSSHA0.490.44< 0.01∗9123234VLSsalinity914VLSChl0.460.4< 0.01∗7322204VLSSST2715VLSSSHA0.430.36< 0.01∗7520189VLSSST2516VLSSSHA0.410.35< 0.01∗8422213VLSdensity1617VLSChl0.500.290.156841177VLSSSHA15VLSSST1Multiple linear regression of VLSDMS
Multiple linear regression (MLR) is used on the campaign-averaged VLS for
SST, salinity, density, Chl, and SSHA to explore VLSDMS variance (Table 1; see Table S4, Supplement, for regression coefficients). Eleven MLR combinations were tested, and all results are significant (p< 0.01), except for the combination of VLSChl, VLSSSHA, and VLSSST (Model 17,
Table 1). Note that the number of available datasets is reduced in the MLR
models that have more input variables, which results in the contribution of
fewer data (campaigns) to the result. The number of input data is
substantially increased if campaign averages are calculated without
filtering the data prior to correlation, so they only contain data where the
two or more correlated variables are co-located. Relaxing the criteria such
that the transects need not be coincident increases the number of campaigns
that can be included in each MLR model. The “relaxed criterion” approach is
less robust but gives similar results to those presented here (see Table S3,
Supplement).
Individual VLSChl and/or VLSSSHA regressions with VLSDMS are outperformed (i.e. R2> 0.47) by four MLR combinations (Models 7–10, Table 1). The combination of VLSdensity and VLSChl (Model 9, Table 1) substantially improves the regression with VLSDMS (adjusted R2 increases to 0.63). MLR Model 9 has the most campaigns (n= 26) of any model and the third highest number of available data transects (n= 224); VLSChl (54 %) and VLSdensity (46 %) make approximately equal contributions to the changes in VLSDMS described by Model 9.
The largest amount of VLSDMS variability explained by the MLR models uses the combination of VLSSSHA, VLSChl, and VLSdensity, improving the adjusted R2 to 0.77 (Model 7, Table 1; Fig. 5d). The dominant parameter in Model 7 is VLSSSHA (52 % of the explained variance), with VLSChl and VLSdensity accounting for 34 % and 14 %, respectively. Combining VLSChl and VLSSSHA (MLR Model 11)
reduces the available input data (n= 20) and does not increase the
explained variance in VLSDMS compared to using only one or other of the input parameters. When paired with one other variable, VLSSSHA and VLSChl dominate the explained variance in MLR models (Models 12–16, Table 1).
DiscussionGlobal statistics
This is the first study of submesoscale seawater DMS variability from a
global perspective. Spatial variability length scale (VLS) analysis is applied to
every ocean basin and at different times of year using a consistent
methodology. Characteristic spatial variability in all six variables (DMS,
SST, salinity, density, Chl, SSHA) occurs at the low mesoscale (in the tens
of kilometres) in all regions. The campaign-averaged VLSDMS ranges from 2–30 km (Table S2, Supplement); this is in general agreement with previous
work (Royer et al., 2015; Nemcek et al., 2008; Asher et al., 2011; Tortell,
2005b; Tortell and Long, 2009; Tortell et al., 2011). There is no
correlation between the campaign-averaged DMS concentration and VLSDMS
(R2= 0.01, p> 0.05), which suggests that understanding the
variability may be a helpful and independent approach to understanding the
processes that control surface ocean DMS.
Regional patterns of DMS variability
VLSDMS is generally above average at the edge of ocean basins, e.g. parts of northwestern Atlantic, northeastern Pacific, and the California coast (Fig. 4, inset). It may be possible that the longer length scales of coastal DMS spatial variability are driven by large phytoplankton blooms, which previous local and regional studies suggest can dominate coastal domains (Asher et al.,
2011; Nemcek et al., 2008). This work does not investigate the detail of
drivers of DMS variability in individual regions or domains.
Open ocean domains such as the subtropical gyres in the Southern Hemisphere
have generally small VLSDMS, a feature not evident in the VLS of the other parameters (Figs. 4 and S2, Supplement). Short length scales of DMS variability in stable stratified biomes offer the opportunity for future work to
re-examine these regions for as yet unidentified drivers of variability.
Most low-latitude DMS data used in this study originate from a single
sampling campaign (e.g. Malaspina Expedition 2010; Royer et al., 2015). To
test if small VLSDMS is a persistent feature in undersampled
subtropical open oceans, more high-resolution observations are needed.
Factors driving temporal DMS variability are not explored in this study.
However, complex VLSDMS fluctuations at high latitudes (e.g. northwestern Atlantic, northeastern Pacific, Southern Ocean; Fig. 4) may be capturing
variations in both space and time; VLSDMS in high-latitude dynamic
regions could be related to the seasonality of biological productivity and
eddy activity (see Asher et al., 2011; Behrenfeld et al., 2019; Bell et al.,
2021; Fox et al., 2020; Gaube et al., 2019; Herr et al., 2019; Lana et al.,
2011; McGillicuddy, 2016). Additionally, it is plausible that VLSDMS in the polar regions may be sensitive to the seasonal impact of sea ice on biogeochemical processes (see Galí et al., 2021; Lannuzel et al., 2020; Stefels et al., 2018). There are not enough repeat measurements made in high-latitude (high seasonal variability) regions to establish the impact of seasonality on VLSDMS. In this study, the only region sampled during different seasons is the northwestern Atlantic (four Atlantic NAAMES campaigns; Bell et al., 2021), and there is not yet compelling evidence of a temporal difference between the VLSDMS of these cruises/seasons. The VLSDMS values of the NAAMES1 transects (November; average = 11.93 km, GSD = 1.76) are
significantly different (p< 0.01) from the transect VLSDMS of NAAMES3 (U= 99, p< 0.01; September; average = 20.89 km, GSD = 1.69) and NAAMES4
(U= 89, p< 0.01; March–April; average = 21.94 km, GSD = 1.57), but not from NAAMES2 (U= 108, p= 0.014; May–June; average = 18.4 km, GSD = 1.56). VLSDMS values of the NAAMES2, 3, and 4 transects are not significantly
different from each other (all p> 0.2).
Drivers of DMS variability
The variance in campaign-averaged VLSDMS data explained by physical
processes (represented by VLSSSHA) is as important as biogeochemical processes (represented by VLSChl), with each parameter able to explain just under half of the VLSDMS (Models 1 and 2, Table 1; Fig. 5). This conclusion contrasts with the findings of Royer et al. (2015), who find that the majority of VLSDMS (65 %) in the low to mid-latitudes is more similar
to the VLS of biological variables that represent biomass and physiology
(Chl and fluorescence) than to the VLS of physical variables. These
contrasting conclusions potentially reflect the fact that length scales of
physical oceanographic variability increase towards the Equator due to the
effects of the Earth's rotation. The Coriolis parameter and therefore Rossby
radius are intrinsically latitudinally dependent (Jacobs et al., 2001). The
longer transects used by Royer et al. (2015) at low to mid-latitudes enable
them to capture scales of variability that may be associated with large
physical features. This point is discussed further in in Sect. 4.5.
A larger proportion of campaign-averaged VLSDMS variability (77 %) can be explained using VLSChl, VLSSSHA, and VLSdensity (Model 7, Table 1) compared to just VLSSSHA or VLSChl. The data included in the VLSSSHA–Chl–density MLR (Model 7, Table 1) are a subset but include
at least one campaign from each major ocean basin (Fig. S4, Supplement) and thus represent a significant relationship with global applicability.
VLSSSHA explains the majority of VLSDMS in the
VLSSSHA–Chl–density MLR (52 %) (Model 7, Table 1) and improves the prediction of changes in VLSDMS compared to using just VLSChl and VLSdensity (Model 9, Table 1). The VLSSSHA–Chl–density MLR (Model 7, Table 1) includes measurements from the NAAMES4 (2018) cruise, which
targeted a substantive eddy and observed a persistent high Chl feature
coincident with elevated DMS levels (Bell et al., 2021). The water mass
within an eddy tends to be retained by the circulation, such that plankton
within the eddy are accumulated under relatively stable physics (upwelling
or downwelling) and consistent biogeochemical conditions (Bell et al.,
2021). Eddies may thus drive conditions where DMS variability is closely
associated with biological activity and a clear covariation in VLS is
observed, even if the relationship between DMS and Chl concentration is less
obvious (della Penna and Gaube, 2019). The relationship between eddy
structure, biogeochemistry, and DMS may explain the link between changes in
VLSDMS, VLSSSHA, and VLSChl. The importance of VLSSSHA for predicting VLSDMS is consistent with results recently reported by
McNabb and Tortell (2022), who apply two independent machine learning
techniques to analyse DMS in the northeastern Pacific. McNabb and Tortell (2022) demonstrate the power of mesoscale eddies for predicting DMS
variability (Spearman correlation coefficients = 0.35 and 0.42, depending
on the machine learning method employed), using the same SSHA product used
in this study (using only summertime measurements, 1997–2017). The
VLSSSHA–Chl–density MLR model coefficients (Model 7, Table S4,
Supplement) are used to predict VLSDMS for the target input
data subset (Fig. 5d). The residuals of predicted VLSDMS are not
systematically biased within the full range of the data (5–25 km).
Implications for global DMS parameterisation
Low-resolution measurements have previously been used to predict mean
spatiotemporal patterns of DMS, both regionally and globally. Several
studies have parameterised DMS as a function of surface mixed layer depth
(MLD), light, and Chl (Vallina and Simó, 2007; Galí et al., 2018;
Simó and Dachs, 2002; Aumont et al., 2002; Anderson et al., 2001;
Belviso et al., 2004a; Aranami and Tsunogai, 2004). For example, Simó
and Dachs (2002) use climatological MLD and remotely sensed Chl to estimate
average DMS concentrations, while Vallina and Simó (2007) use
climatological MLD, surface irradiance, and light attenuation to estimate
surface DMS from the “solar radiation dose”. Galí et al. (2018) employ
an algorithm driven by climatological Argo MLD and satellite-derived Chl,
SST, and photosynthetically active radiation (PAR). Only the latter
algorithm was additionally validated at finer resolution using
non-climatological data to enable regional time series studies (Galí et
al., 2019). SSHA reflects surface mixing and changes to the MLD (Gaube et
al., 2019). This study supports the choice of key variables used in existing
empirical parameterisations by demonstrating that, even on small scales,
physical mixing (SSHA, density) and biological activity (Chl) explain a
large portion of surface seawater DMS spatial variability in the global
ocean.
The DMS parameterisations with global coverage that rely on remote and
autonomous observations predict spatially and seasonally averaged surface
seawater DMS reasonably well (e.g. Galí et al., 2018; Simó and
Dachs, 2002; Vallina and Simó, 2007). However, some studies have
questioned whether such parameterisations are overly reliant on
spatial and/or temporal averaging, often to 1∘ and/or monthly resolution (e.g.
Derevianko et al., 2009). The spatiotemporal averaging used to develop
global parameterisations may lead to an overconfidence in current
predictive capabilities because key parameters are not included.
Statistically significant MLR relationships in this study are obtained once
the transect data are averaged by campaign, and the average VLS for all six
variables in our study is tens of kilometres. Using VLS analysis to assess
the covariation of parameters at the submesoscale provides insights that
can help to improve global parameterisations. Our results indicate that
patterns of mesoscale and submesoscale DMS variability, particularly those
associated with SSHA, will be obscured at the 1∘ resolution of
most global parameterisations, highlighting the importance of modelling work
at finer resolutions (e.g. Galí et al., 2019; McNabb and Tortell,
2022, 2023). Regional studies have tested empirical predictive relationships
for DMS with varying degrees of success (Asher et al., 2011; Royer et al.,
2015; Bell et al., 2006, 2021).
Study limitations and unidentified drivers of DMS variability
This work provides as comprehensive an assessment of DMS variability across the
global ocean as existing data allow; however, many regions have not yet been
sampled at high enough resolution to permit an assessment of VLSDMS. For example, only 7 of the 37 campaigns in this study have made high-resolution DMS measurements in low-latitude waters (30∘ N–30∘ S). There is a seasonal sampling bias within the DMS database, and the northwestern Atlantic is the only region to have been
assessed for VLS throughout the seasonal cycle (Bell et al., 2021). More
data are needed.
Satellite-derived VLSChl and VLSSSHA have been used to predict VLSDMS (e.g. Model 7, Table 1), but this relies on the assumption that the satellite-retrieved data are representative of phytoplankton productivity and eddy activity throughout the research cruise/campaign. Satellite retrievals for Chl with higher than monthly temporal resolution or, in the case of SSHA, higher than 0.17∘ spatial resolution may improve the ability to explain variance in VLSDMS.
Transect lengths between 100 and 199 km are used to ensure comparability
between datasets/regions because VLS results from previous studies appear to
be sensitive to the length of data transect (see Fig. S5, Supplement). However, by limiting the transect length, it is difficult to
identify large eddies using VLSSSHA. Eddy length scales are typically larger at low latitudes due to the dependence of the Coriolis parameter on latitude (Chelton et al., 1998). The maximum VLS in this study is between 50
and 99.5 km (half the transect length), which is long enough to capture the
eddy variability at latitudes where the eddy length scale is related to the
Rossby radius of deformation, i.e. poleward of 30∘, where
the deformation radius is < 30 km (Eden, 2007). Equatorward of
30∘, eddy length scales are not well predicted by the Rossby
radius of deformation and can exceed 50 km (Tulloch et al., 2011; Scott and
Wang, 2005; Klocker et al., 2016; Eden, 2007; Rhines, 1975). The
VLSSSHA analysis approach used in this study is designed to identify
the dominant scale of variability in physical features up to 50–99.5 km;
therefore, it may not capture the full extent of variability associated with
large eddies at low latitudes. Large eddies will, however, still be captured
in the VLSSSHA analysis where a transect segments an eddy without
passing through its centre. We also note that although SSHA is used to
represent eddy features, at the Equator, stratification and strong westward
currents tend to dominate SSHA variability rather than rotation and eddy
transport (Williams and Follows, 2011).
In the subtropical gyres, VLSDMS is typically small (< 10 km;
Fig. 4), which is qualitatively consistent with the short (days) response
time of DMS to perturbations in the dynamic equilibrium of DMS production
and consumption in these waters (Galí and Simó, 2015); VLSDMS
in subtropical waters does not correspond well with the VLS of any of the
other parameters (Fig. S3, Supplement). Cycling of reduced
sulfur compounds in subtropical waters is well documented to be part of a
different biogeochemical regime compared to productive, higher-latitude
waters (e.g. Galí and Simó, 2015; Toole and Siegel, 2004). In
stable oligotrophic regions where there is less variability in physical
mixing and phytoplankton productivity, VLSDMS could thus be dominated
by alternative parameters that drive variability in the biological cycling
of DMS such as zooplankton grazing (Simó et al., 2018) and microbial
organosulfur metabolism (Nowinski et al., 2019; Cui et al., 2015; Alcolombri
et al., 2015).
The so-called “summer paradox” describes the seasonal misalignment between
maximum concentrations of phytoplankton biomass and DMS in low-latitude
waters, and it has been challenging to model (e.g. Galí and Simó,
2015; Polimene et al., 2012; Toole et al., 2008; Vallina et al., 2008). In
these areas, characterised by low seasonal amplitude in phytoplankton
biomass, changes in phytoplankton species succession and physiological
stress control DMS production yields and rates and, ultimately, DMS
seasonality. By contrast, aggregated loss processes exhibit low seasonal
variability and are insufficient to explain large-scale DMS seasonality in
summer paradox areas (Galí and Simó, 2015). Previous studies
observed important short-term variations in the balance between DMS sources
and sinks in oligotrophic waters, concomitant with meteorological forcing
(Royer et al., 2016). Hence, it is plausible to hypothesise that subtle
changes in this balance can explain some of the variance in VLSDMS.
Light exposure in surface waters influences plankton physiological
production and stress, photochemical reactions, and bacterial activity and
thus has a significant impact on the cycling of reduced sulfur in
oligotrophic regions (see Toole and Siegel, 2004; Vallina et al., 2008).
These factors have not been included in the present study.
Conclusions
This study presents a comprehensive and objective analysis of DMS
variability based on a large global dataset of high-frequency observations
at the local/regional scale. The work shows that the variability length scale
for DMS is typically small (< 30 km) and that a substantial
proportion of the campaign-averaged variance can be explained by the VLS of
key biological (Chl) and physical (density, SSHA) observations (Model 7,
Table 1). The results improve confidence in the validity of the biological
and physical parameters used to currently parameterise seawater DMS at large
scales and used in many global climate models (e.g. Bock et al., 2021;
Galí et al., 2018; Mulcahy et al., 2020; Simó and Dachs, 2002).
However, there is substantial variability in VLSDMS when assessing
individual transects, which suggests that unaccounted-for variables are also
important (e.g. light, wind speed, microbial diversity and activity).
Making high-frequency measurements of these parameters at the same time as
high-frequency DMS measurements may help to elucidate their role in DMS
cycling.
Data availability
DMS and ancillary in situ data (SST and salinity) are sourced from the global surface seawater DMS database (GSSDD; https://saga.pmel.noaa.gov/dms/) and supplied by authors Rafel Simó, Martí Galí, Anoop S. Mahajan (Malaspina Expedition in 2010–2011, M10), Thomas G. Bell (North Atlantic Aerosol and Marine Ecosystem Study in 2015–2018, NAAMES; 10.5067/SeaBASS/NAAMES/DATA001, Behrenfeld et al., 2018), and George Manville (Southern oCean SeAsonaL Experiment in 2019, SCALE). Requests for access to M10 and SCALE DMS data can be sent to the corresponding authors (George Manville and Thomas G. Bell). Satellite data are available in online NASA repositories for chlorophyll a (10.5067/AQUA/MODIS/L3M/CHL/2018, NASA Goddard Space Flight Center, 2018) and sea surface height anomalies (10.5067/SLREF-CDRV2, Zlotnicki et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-20-1813-2023-supplement.
Author contributions
GM, PRH, TGB, and JPM devised the study. GM conducted the analysis and interpretation and wrote the paper, with input from PRH and TGB. JPM, MG, and RS provided insight and helped to improve the analysis and
interpretation. All co-authors contributed to the paper.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was supported by the UK Natural Environmental Research Council
(through a PhD studentship for George Manville: NE/R007586/1). The contribution of Thomas G. Bell was via the NAAMES (a NASA Earth Venture Suborbital program, NNX#15AF31G) and CARES (NERC: NE/W009277/1) projects. Jane P. Mulcahy was supported by the Met Office
Hadley Centre Climate Programme funded by BEIS and also received funding
from the European Union's Horizon 2020 research and innovation programme
under grant agreement no. 101003536. This research was supported by
the Spanish National Plan for Scientific and Technical Research and Innovation through project BIOGAPS (CTM2016-81008-R) to Rafel Simó, through project DMS-Cons (202230I123) to Martí Galí, and through the Severo Ochoa Centre of Excellence grant (CEX2019-000928-S) to the ICM-CSIC. Rafel Simó is a holder of a European Research Council Advanced Grant
(ERC-2018-ADG-834162) under the EU's Horizon H2020 research and innovation
programme. The Indian Institute of Tropical Meteorology is funded by the
Ministry of Earth Sciences, Government of India.
We thank the contributors of high-frequency DMS data (Stephen Archer, Alycia Herr, Tereza Jarníková, James Johnson, Christa Marandino, Sarah-Jeanne Royer, Eric Saltzman, Philippe Tortell, Miming Zhang) for
making their DMS data available (see Table S2, Supplement for
full details) via the Global Surface Seawater DMS Database. George Manville and Thomas G. Bell thank
those involved in the 2019 SCALE (Southern oCean SeAsonaL Experiment)
spring–summer campaign, including Thomas Ryan-Keogh and
Marcello Vichi, Knowledge Bengu, and the crew of the SA Agulhas II. Special thanks go to Sandy Thomalla for
facilitating George Manville and Thomas G. Bell's involvement in the SCALE project and to Tebatso Martin Moloto for assisting George Manville in the onboard measurement of DMS.
Financial support
This research has been supported by the Natural Environment Research Council (grant nos. NE/R007586/1 and NE/W009277/1), Met Office
Hadley Centre Climate Programme funded by BEIS, the Horizon 2020 (grant nos. 101003536 and ERC-2018-ADG-834162), the Spanish National Plan for Scientific and Technical Research and Innovation (grant nos. CTM2016-81008-R, 202230I123, and CEX2019-000928-S), and the Indian Institute of Tropical Meteorology (IITM).
Review statement
This paper was edited by Peter Landschützer and reviewed by two anonymous referees.
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