Temporal trends in sea surface temperature gradients in the South Atlantic Ocean

Remote Sensing of Environment 194 (2017) 100–114

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Temporal trends in sea surface temperature gradients in the South Atlantic Ocean Marouan Bouali* , Olga T. Sato, Paulo S. Polito Instituto Oceanográfico da Universidade de São Paulo (IOUSP), São Paulo, Brazil

A R T I C L E

I N F O

Article history: Received 27 July 2016 Received in revised form 3 February 2017 Accepted 11 March 2017 Available online xxxx

A B S T R A C T In this study, we investigate the spatial and temporal characteristics of Sea Surface Temperature (SST) gradients in the South Atlantic Ocean (SAO) using satellite data. 12 years (2003–2014) of high resolution synoptic SST images acquired from NASA’s MODIS instrument onboard Terra and Aqua platforms were processed to mitigate quality issues related to stripe noise and cloud misclassification and then compiled into monthly, yearly, and decadal maps of SST gradient magnitudes. We used these composite maps and corresponding time series to identify regions with strong frontal activity and characterize the seasonal and long term evolution of thermal gradients. While a clear seasonal cycle was observed in most subregions of the SAO, the satellite data do not suggest significant interannual variability or long term changes in the magnitude of thermal fronts in the SAO except in the upwelling region of Cape Frio Brazil, where SST gradients have increased at an approximate rate of 1% per year since 2003. © 2017 Elsevier Inc. All rights reserved.

1. Introduction Ocean fronts are commonly defined as narrow boundary regions that separate water masses with distinctive biological, chemical, or physical properties (Legeckis, 1978). These discontinuities appear as elevated gradients in the horizontal or vertical distribution of one or multiple ocean biophysical parameters such as temperature, nutrient concentration, salinity, or turbidity. Ocean fronts can be observed at different spatial and temporal scales (Belkin, 2009). For example, large scale oceanic fronts associated with the thermohaline circulation extend down to thousands of meters and persist for several months whereas three dimensional microscale turbulence occurs at scales of a few meters and dissipates within minutes. A wide variety of geometrical patterns are associated with near-surface horizontal fronts and can be seen in satellite imagery representing ocean parameters like sea surface temperature (SST) or chlorophylla concentration. These frontal features typically appear as meanders, filaments and eddies of different lengths and diameters. Mesoscale to miscroscale fronts have a well documented impact on ocean biological dynamics (Holloway and Denman, 1989; McGillicuddy et al., 2007, 2003, 1998) through biomass transport and mixing in both vertical and horizontal directions. As such, ocean fronts can serve as a proxy to determine the geographical boundaries of marine

* Corresponding author. E-mail address: [email protected] (M. Bouali).

http://dx.doi.org/10.1016/j.rse.2017.03.008 0034-4257/© 2017 Elsevier Inc. All rights reserved.

ecosystems (Belkin et al., 2009). In fact, intense mixing between ocean layers acts as a catalyst of primary productivity which, in turn can influence the distribution of a variety of intertwined marine life that includes phytoplankton, zooplankton, pelagic fish, sea birds and marine mammals (Acha et al., 2004). There is also evidence that oceanic fronts have a major role in ocean-atmosphere interactions. Thermal fronts influence the thermodynamic structure of the marine atmospheric boundary layer (MABL), surface momentum, latent and sensible heat fluxes as well as cloud properties (Sweet et al., 1981; Pyatt et al., 2005; Small et al., 2008). In some regions, the effects of ocean fronts on air-sea exchange can go beyond the MABL and reach the troposphere to create rainbands and thunderstorms (Hobbs, 1987). Although coupling effects between the ocean and the atmosphere are predominant in the mesoscale domain, turbulent fluctuations over smaller scales also contribute to the transfer of heat between the ocean and atmosphere to the extent of affecting the boundary layer (Rouault et al., 2003). In fact, recent observational and modeling studies (Thomas and Ferrari, 2008; Ferrari, 2011; Levy et al., 2012) have shown the existence of an intermediate class of motion in the ocean surface that lies between mesoscale flows and microscale turbulence. These so-called submesoscale fronts whose spatial scales range from 1 to 10 km arise in the upper-ocean from frontogenesis, i.e., a mechanism that involves stirring and straining of larger scale flows in quasi-geostrophic balance. Because submesoscale fronts can influence weather and climate patterns, improved understanding of ocean dynamics and its long term evolution at these horizontal scales is of major importance. In this context,

M. Bouali et al. / Remote Sensing of Environment 194 (2017) 100–114

substantial work based on remotely sensed data has been conducted to observe such features over specific regions and characterize their spatial and temporal properties. Castelão et al., 2006 used 5 years of data from the Geostationary Operational Environmental Satellites (GOES-10) and compiled a bi-monthly climatology of SST fronts. In that, they identified seasonal patterns of upwelling fronts over the California Current System (CCS). A similar analysis over the CCS based on 29 years of Advanced Very High Resolution Radiometer (AVHRR) Pathfinder SST and 14 years of chlorophyll-a concentration from the Sea-Viewing Wide Field-of-View Sensor (SeaWIFS), the Moderate Resolution Imaging Spectrometer (MODIS) onboard Aqua, and the European Space Agency’s Medium Resolution Imaging Spectrometer (MERIS) was conducted by Kahru et al., 2012 in conjunction with large scale SST fields and sea level pressure anomalies. A comprehensive survey of SST fronts over the global ocean is provided in Belkin and Cornillon, 2007 based on 12 years of AVHRR Pathfinder SST at 9 km resolution. Despite the existence of numerous and well studied current systems in the South Atlantic Ocean (SAO), limited work has focused on the analysis of trends in thermal gradients from remotely sensed imagery (Waluda et al., 2001; Acha et al., 2004; Saraceno et al., 2005. In this study, we investigate the spatial and temporal characteristics of SST gradients using high resolution satellite SST observations over the SAO to 1) locate regions with intense thermal fronts, 2) characterize the seasonal cycle and the interannual variability of thermal fronts over those regions and 3) determine whether significant long term trends in the magnitude of thermal gradients can be identified. Many studies in the literature rely on the detection of fronts in synoptic images to generate maps representing the probability of observing a front at a given location (Hickox et al., 2000; Belkin and Cornillon, 2005; Castelão et al., 2006; Belkin et al., 2009; Kahru et al., 2012). The detection of ocean fronts in satellite imagery is commonly done using either gradient methods (Moore et al., 1999; Kostianoy et al., 2004; Breaker et al., 2005; Belkin and O’Reilly, 2009) that rely on the magnitude of the gradient field or the seminal approach of Cayula and Cornillon (1992) which uses a moving window to analyze local histograms and identify transition zones. Automated front detection algorithms systematically require the selection of parameters, i.e., a threshold on the gradient magnitude or the size of a moving window used for the analysis of local histograms. Regardless of the selected approach, reliable classification of frontal pixels depends on the use of parameters that are sensitive to the sensor spatial resolution, the signal quantization as well as the level of Gaussian noise and striping artifacts. This is a major challenge for the comparison of results derived from different instruments. Therefore, in this study we consider only the magnitude of the thermal gradient and use its temporal mean to identify regions with strong frontal activity. Finally, it should be mentioned that the work described here is purely an observational effort aiming towards the characterization of spatio-temporal statistics of SST gradients. Interpretations based on physical processes involved in these observations are beyond the scope of this study. Identification of the primary cause of the temporal evolution of frontal activity for example, requires the analysis of additional physical parameters of the ocean and atmosphere and will be explored in a future study. This analysis of past properties of thermal fronts is mainly intended for statistical inferences of its impact on other Earth systems.

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potential for such applications. While LWIR SST is known to be more accurate than SWIR SST, the incentive behind the selection of the 4 lm instead of the 11 lm MODIS SST is due to the differences in the formulation of SST retrieval algorithms and, as discussed in the next section, its impact on the observed gradient field. The MODIS instruments were launched onboard Terra and Aqua platforms in 2000 and 2002 respectively and have since been providing a continuous stream of products for several Earth science disciplines, including physical oceanography. Provided additional post-processing (see next section), MODIS incorporates technological features that are highly beneficial for the analysis of SST gradients. The signal acquired by MODIS has a spatial resolution of 1.1 km at nadir and a higher quantification rate (i.e., 12 bits) than instruments like AVHRR and GOES which were widely used for frontal studies. Further, the temporal overlapping of Terra and Aqua missions allowed us to compare the results from both instruments for cross-consistency. The dataset was composed of 12 years of both Terra and Aqua MODIS 5 min granules of level 2 nighttime 4 lm SST acquired from 2003 to 2014 over the SAO. Overall, approximately 330 thousand images of 1354 × 2030 pixels were processed in this study. All the data were downloaded from NASA’s Ocean Color website http://oceancolor. gsfc.nasa.gov/.

2.1. SST formulation and its effect on gradient fields Estimation of SST from satellite observations is typically done by combining brightness temperatures (BTs) of different spectral channels to compensate for water vapor and atmospheric effects. Many empirical formulations have been devised to generate SST products that are statistically consistent with in situ measurements. Among these formulations is the extensively used nonlinear SST (NLSST) algorithm (Walton, 1988) that takes the following form:

NLSST = a1 + a2 BT11 + a3 SSTref (BT11 − BT12 ) + a4 (BT11 − BT12 )(sec(h) − 1)

(1)

where BT11 and BT12 correspond to satellite BTs acquired at 11 lm and 12 lm, a1 to a4 are coefficients derived from in situ measurements, h is the satellite zenith angle and SSTref is a first guess or “reference” SST, typically a level 4 cloud-free SST field. The differential term SSTref (BT11 − BT12 ) used for atmospheric correction may affect the analysis of long term trends in SST fronts. In fact, similarly to what was observed with AVHRR channels 4 and 5 (Bowen et al., 2002), images resulting from the difference between MODIS BT11 and BT12 display a significant amount of Gaussian noise. Consequently, the use of the baseline SSTref leads to a nonlinear amplification of the noise and thus of the magnitude of the retrieved SST gradient field. What’s more, the NLSST formulation is such that any trend in SST values could be artificially transferred to the trend of SST gradient magnitude and lead to an erroneous interpretation of the temporal statistics of SST gradients. Therefore, we used a Multichannel SST (MCSST) (Llewellyn-Jones et al., 1984) product that does not incorporate a first guess SST and uses spectral channels with minimal amounts of Gaussian noise. For the case of the MODIS level 2 Short Wave Infrared (SWIR) 4 lm SST, the following form is used:

2. Data and methods In this work, we used a specific dataset of SST satellite observations namely, the level 2 4lm (nighttime) SST derived from NASA’s MODIS sensor. It should be mentioned that among MODIS SST products, the Short-Wave Infrared (SWIR) level 2 SST, unlike the Long-Wave Infrared SST (LWIR) (also referred to as the 11lm SST) was rarely used for the analysis of ocean fronts, despite its strong

MCSST = a1 + a2 BT39 + a3 (BT39 − BT4 ) + a4 (BT39 − BT4 )(sec(h) − 1)

(2)

where BT39 and BT4 are BTs centered at 3.9 lm and 4 lm respectively.

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2.2. Data processing There are several limitations in the quality of MODIS level 2 SST products that make them unsuitable for direct use in some applications. While these do not affect the statistical accuracy of SST values, they pose a serious challenge to the analysis of thermal fronts that are based on the gradient operator. As demonstrated further, extensive post-processing is required to mitigate these issues for reliable interpretability of the spatio-temporal characteristics of SST gradients.

with clouds. Instead of using a threshold on the gradient magnitude computed with only two orthogonal components, we compute a metric composed of gradients in all 8 directions associated with the nearest neighbors of a given pixel. For pixels with similar gradient magnitudes, this metric takes small values over ocean fronts and high values over cloudy pixels. In the following, we denote ss (i, j) the destriped SST at pixel (i, j) in the swath projection. Pixel-to-pixel variations at (i, j) are computed in k = 1..8 different directions using a finite forward differences scheme as follows: d1 ss (i, j) = ss (i − 1, j) − ss (i, j)

2.2.1. Stripe noise correction Whiskbroom scanners like MODIS acquire images using a continuously rotating double-sided mirror and an array of detectors. Minor differences in the mirror sides and detectors spectroradiometric responses lead to striping in the captured images. This image artifact appears as artificial lines and differs from standard Gaussian noise in that it has a directional signature in the satellite cross-track direction. As such, due to the presence of stripe noise, the magnitude and orientation of the observed gradient field differ from that of the true gradient field. Because the analysis of ocean fronts is based on the gradient field characteristics, it is crucial to correct stripe noise in the full resolution swath projected data prior to further processing. In this context, an ideal destriping algorithm should be able to remove the effects of striping on the gradient field, preserve the characteristics of ocean structures and avoid the introduction of blur or other processing artifacts like ringing or staircase noise (Bankman, 2000). Such requirements are met by the algorithm described in Bouali et al., 2015 whose results demonstrated the importance of destriping the swath projected data and its positive impact in full resolution and down-sampled synoptic maps of SST gradient magnitudes. They also showed that application of the algorithm in the swath projection removes an artificial bias of the gradient field orientation towards the cross-track direction. In this study, stripe noise in MODIS level 2 SST images was removed using the algorithm described in Bouali et al., 2015 prior to further processing. 2.2.2. Cloud masking SST fields derived from satellite data are usually distributed with a cloud mask or additional data that quantifies its quality, i.e., typically a quality flag with discrete values. In both cases, these are generated from a series of tests that quantify the likelihood of clouds or the statistical accuracy of retrieved SST for each pixel. For example, MODIS level 2 SST files include a quality flag with discrete values ranging from 0 to 4. Users are encouraged to select SST pixels with a quality flag value of 0 which represents the best quality. However, visual analysis of unflagged SST fields shows that in many cases, pixels along sharp ocean fronts are attributed a low quality flag value. This is likely due to one specific test that compares the retrieved SST with a lower resolution level 4 SST. As a reference field for quality control, MODIS SST products use the Reynolds Optimally Interpolated SST (OISST) which has a spatial resolution of 25 km (Reynolds et al., 2007). Pixels where the absolute difference between the level 2 and the level 4 SST exceeds a given threshold are considered suspicious. If the selected threshold is overly conservative, small scale features that do not appear in the lower resolution level 4 SST fields are erroneously classified as clouds or bad data and given a quality flag higher than 0. As a result, a significant number of sharp thermal fronts in synoptic observations are discarded. This can have a detrimental impact on the interpretation of climatological maps of SST gradient magnitudes. To overcome this issue, we developed an alternative cloud masking strategy oriented towards improved discrimination between sharp SST fronts and clouds. We rely on the observation that high pixel-to-pixel variations related to ocean fronts have a well defined orientation as opposed to those associated

d2 ss (i, j) = ss (i − 1, j + 1) − ss (i, j) d3 ss (i, j) = ss (i, j + 1) − ss (i, j) d4 ss (i, j) = ss (i + 1, j + 1) − ss (i, j) d5 ss (i, j) = ss (i + 1, j) − ss (i, j) d6 ss (i, j) = ss (i + 1, j − 1) − ss (i, j) d7 ss (i, j) = ss (i, j − 1) − ss (i, j) d8 ss (i, j) = ss (i − 1, j − 1) − ss (i, j) (3) For preliminary cloud masking in the swath projection, the absolute value of the average of pixel-to-pixel variations in all 8 directions can be used as an index, denoted hereafter icloud and defined at pixel (i, j)as: icloud (i, j) =

1 8

       s d s (i, j)   k  

(4)

k=1..8

For pixels that would otherwise have similar gradient magnitudes, icloud takes small values over ocean fronts and high values over cloudy pixels. From the analysis of several representative granules, a threshold of 0.5◦ C/pixel was selected and pixels that satisfy the following condition: icloud (i, j) > 0.5

(5)

were considered cloudy. Our processing chain can be summarized as follows. First, in the swath projection, level 2 SST images at full resolution are corrected for stripe noise using the algorithm described in Bouali et al., 2015. Pixels where icloud exceeds a threshold of 0.5◦ C/pixel are classified as cloudy and given a NaN value (Not a Number). We then used NASA’s Ocean Biology Processing Group (OBPG) SeaDAS software to reproject the level 2 SST field into a Mercator lat/lon map with a spatial resolution 0.05◦ . This downsampling allows to reduce the volume of data to be processed and also serves as a means to improve the detection of clouds. In fact, we used bicubic resampling to create a dilation-like effect of cloudy pixels, i.e., an automatic propagation of NaN values associated with clouds to neighboring pixels in the lower resolution grid. To further detect clouds that are too homogeneous to affect gradient values (typically cirrus clouds), we used the UK Met Office Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) SST. Pixels where the absolute difference between the projected SST with 0.05◦ resolution and OSTIA SST is higher than 4◦ C were masked out. The synoptic SST in map projection obtained from the described post-processing is denoted sm . For a pixel (i, j) in the 0.05◦ grid, the SST gradient magnitude is computed using forward finite differences in both zonal and meridional directions as:  m    ∇s (i, j) = sm (i − 1, j) − sm (i, j) 2  1/2 + sm (i, j − 1) − sm (i, j)

(6)

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Fig. 1. Seasonal map of summer 2007 of the SST gradient magnitude in the Brazil-Malvinas confluence region obtained from the (a) original product using quality flag 0 and (b) from the data processed for stripe noise correction and improved cloud masking. Annual map of 2007 from (c) the original data and (d) from the processed data. The composite maps are obtained as a temporal mean of local gradient magnitude derived from synoptic observations.

At each grid point, the magnitude of the SST gradient field is averaged in time as:   ∇sm (i, j) =

 1   m ∇s (i, j) t Nobs t

(7)

using all SST synoptic observations sm obtained during a time interval t of a month, season, year or 12 years. In the previous equation, t Nobs corresponds to the number of clear-sky observations in the selected time interval t. This allows us to generate monthly, seasonal, annual and 12-year climatologies of SST frontal activity in the South Atlantic Ocean. It should be noted that gradient magnitudes computed on synoptic lat/lon maps from Eq. (6) correspond to degrees Celsius/decimal degrees. Monthly means of gradient magnitudes were converted to degrees Celsius/kilometers using a latitude dependent scaling function (1 + sec(lat))/2. While this is only an approximation of the gradient magnitude in degrees Celsius/km unit, it does not affect the study of trends which is the primary focus of this study. The impact of our processing chain on monthly and annually averaged SST gradient magnitudes is illustrated in Fig. 1 and shows significant differences compared to results obtained from original products. This demonstrates the importance of mitigating quality issues in lower level products before the production of composite maps. 3. Results The first step of our study consisted of identifying regions with pronounced frontal activity using the statistical properties of the

12-year composite map of SST gradient magnitudes computed over the global South Atlantic Ocean (SAO). The 12-year mean of SST gradient magnitudes over the SAO follows a log-normal distribution with a mean l of 0.057◦ C/km and a standard deviation s of 0.017◦ C/km. In this study, we defined frontally active regions as those where the 12-year mean exceeds a threshold of l + 1.5s, i.e., 0.08◦ C/km. These areas indicate either the existence of persistent oceanic features related mostly to mesoscale fronts or strong transient thermal gradients associated to submesoscale processes. The regions selected for further study include: (a) Cape Frio upwelling region, (b) the Brazil-Malvinas Confluence region, (c) the Cape Horn current, (d) the merger of the Subantarctic front (SAF) and the Antarctic Polar front (PF), (e) the Agulhas retroflection, (f) the Agulhas current and (g) the Benguela upwelling systems. These will be discussed in turn below. • (a) The Cape Frio upwelling region The Brazilian Southeast coastal area that extends from Cape Frio (23◦ S,42◦ W) to the delta of the Rio Doce (19.3◦ , 39.5◦ W) is a well studied oceanographic area. It is characterized by coastal upwelling caused by two types of processes. The first of these is offshore Ekman transport due to uniform along-shore winds that bring deeper and colder waters of the South Atlantic Central Water (SACW) toward the surface in the inner shelf region (Castelão and Barth, 2006). This can lead to a decrease in surface temperatures of up to 14◦ C (Reynolds et al., 1996) which generates sharp horizontal thermal gradients near the coast. Secondly, coastal upwelling due to surfacing of cold water masses from the SACW can be caused by cyclonic meanders of the

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M. Bouali et al. / Remote Sensing of Environment 194 (2017) 100–114

Fig. 2. 12-year composite maps of SST gradient magnitude over the South Atlantic Ocean computed using Terra and Aqua MODIS data. Squares and corresponding close-up show regions with intense frontal activity.

Brazil Current (BC) even under calm-wind conditions (Campos et al., 2000; Calado et al., 2010). In Fig. 2, we observe strong SST gradients offshore Cape Frio (23◦ S, 42◦ W) and Cape São Tomé (22◦ S, 41◦ W). The 12-year mean of gradient magnitude reaches a maximum of 0.14◦ C/km immediately in the vicinity of the capes and gradually decreases offshore towards the south and southwest of Cape São Tomé and Cape Frio respectively. The direction of decrease of thermal gradients is likely associated with the advection of coastal upwelled water plumes by southwestward wind-driven currents in this region. Further north, an extended filamentous pattern of intense gradients can be seen departing near the estuary of Rio Doce (19◦ 36’S, 39◦ 47’W). The spatial distribution of this feature follows approximately the continental shelf break and the cyclonic meanders associated with the Brazil Current (BC). It extends southward for approximately 300 km with 12-year mean of SST gradient magnitudes remaining consistently between 0.1◦ C/km and 0.15◦ C/km. We note that Fig. 1 does not show any distinctive feature possibly connected to the Vitória Eddy, which is regarded as a permanent feature in Schmid et al. (1995) ; Arruda et al. (2013). This likely indicates that this vortex does not affect the spatial distribution of SST in the region. • (b) The Brazil-Malvinas confluence region The Brazil-Malvinas confluence (BMC) is a region marked by large eddy kinetic energy (Xu et al., 2011) and an enormous contrast in surface heat flux (Sato and Polito, 2014) where warm waters of the

Brazil Current flow southward and meet the cold northward-flowing Malvinas Current. This area extends from 30◦ S to 50◦ S and is marked by strong zonal gradients in the SST. In the 12-year composite map of Fig. 2, we observed that the frontal activity in the BMC appears off the coast of Argentina and Uruguay and is distributed in an∼ 50 km wide zonal band at 33.5◦ S where the 12-year mean of gradient magnitude ranges between 0.11 and 0.13◦ C/km. Further south, this band gradually widens up and reaches ∼175 km at 38◦ S. Beyond this latitude, i.e, the northern limit of the Malvinas Current (MC) the frontal activity weakens (i.e., 0.08–0.10◦ C/km) and agglomerates along two distinctive high-gradient bands. This observation is in agreement with the work described in Franco et al. (2008), Piola et al. (2013) where the authors used hydrographic data and current Table 1 Seasonal evolution of SST gradient magnitude from Terra MODIS. Region

SST gradient magnitude (◦ C/km) from Terra MODIS

–

Summer

Fall

Winter

Spring

Annual

CV

Cape Frio Cape Peninsula Namibia Coast Angola Coast Brazil-Malvinas Agulhas retroflection SAF ∩ PF Agulhas current Cape Horn

0.128 0.138 0.111 0.104 0.084 0.092 0.088 0.086 0.089

0.088 0.121 0.094 0.096 0.105 0.100 0.093 0.100 0.098

0.068 0.081 0.079 0.077 0.107 0.099 0.098 0.097 0.095

0.102 0.097 0.100 0.081 0.101 0.097 0.091 0.089 0.093

0.096 0.109 0.096 0.090 0.099 0.097 0.092 0.093 0.094

25% 23% 14% 14% 10% 4% 5% 7% 4%

M. Bouali et al. / Remote Sensing of Environment 194 (2017) 100–114 Table 2 Seasonal evolution of SST gradient magnitude from Aqua MODIS. Region

SST gradient magnitude (◦ C/km) from Aqua MODIS

–

Summer

Fall

Winter

Spring

Annual

CV

Cape Frio Cape Peninsula Namibia Coast Angola Coast Brazil-Malvinas Agulhas retroflection SAF ∩ PF Agulhas current Cape Horn

0.131 0.134 0.106 0.104 0.081 0.091 0.084 0.085 0.082

0.090 0.119 0.091 0.094 0.105 0.099 0.092 0.102 0.090

0.070 0.077 0.075 0.074 0.107 0.098 0.097 0.096 0.088

0.105 0.090 0.095 0.080 0.098 0.097 0.087 0.088 0.083

0.099 0.105 0.091 0.088 0.098 0.09 0.090 0.093 0.086

26% 25% 14% 15% 12% 4% 6% 8% 4%

measurements to show that the MC flow is mainly concentrated in two jets that have a width of the order of 10–20 km and follows the 200 and 1400 m isobaths.

• (c) The Cape Horn current

South of 44◦ S, along the western frontal branch of the MC, the 12-year mean magnitude of SST gradients diffuses and drops from 0.08◦ C/km to 0.05◦ C/km. A narrower structure continues south, rounds the eastern side of the Falkland Islands and starts to intensify around 53◦ S as it reaches the Cape Horn Current, a current that contributes to the exchanges of waters masses from the South-

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east Pacific to the Southwest Atlantic ocean (Acha et al., 2004). The 12-year mean of thermal gradients along this current is consistently higher than 0.08◦ C/km and reaches significantly higher values (∼0.13◦ C/km) about 15–20 km south of Isla de Los Estados. • (d) The merger of the Subantarctic front (SAF) and the Antarctic Polar front (PF) South of the Zapiola Rise, visible in Fig. 2 as a near elliptical area (∼1150 km diameter) of lower frontal activity compared to its surroundings, a wide (≥150 km) band of high SST gradients can be seen. This feature is centered at 49◦ S and extends approximately from 55◦ W to 30◦ W with 12-year mean of thermal gradients varying between 0.08 and 0.13◦ C/km. Maximum values of SST gradients are located at the meridional center of this frontal structure and gradually decrease along its south and north. Strong gradients in this region can likely be attributed to the Antarctic Polar Front (PF). However, the various studies dedicated to the localization of the PF show significant discrepancies in its mean path/position (Moore et al., 1999). Given the small distance between the mean path of the PF and the Subantarctic Front (SAF) in this zonal region, it could also be hypothesized that the observed high-gradient band depicts strong frontal activity due to the shorter-time scale interaction of water masses located north and south of the SAF and PF mean positions respectively. This hypothesis could be further supported by the appearance further west of another high-gradient feature located between 15◦ W and 5◦ W, a region where the distance between the SAF and the PF is known to reduce again (Giglio and Johnson, 2016). • (e) The Agulhas retroflection and (f) Agulhas current

Fig. 3. Seasonal maps of SST gradient magnitude in the Cape Frio upwelling region. CF, Cape Frio; CST, Cape São Tomé; RD, Rio Doce.

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Fig. 4. Seasonal maps of SST gradient magnitude off Cape Peninsula.

The southern termination of the Agulhas current is located in a transition zone between two subtropical gyres, i.e., the South Atlantic gyre and the Indian Ocean gyre. The contrast in temperature of water masses from two distinct oceans meeting in this region results in highly dynamic ocean processes with major impacts on the exchange of heat and moisture with the atmosphere as well as rainfall patterns (Lutjeharms, 2006). As it reaches the southern tip of the African continental shelf, the Agulhas current turns back on itself and flows back towards the Indian Ocean gyre. This looping region is known as the Agulhas retroflection (AR). In Fig. 2, both the Agulhas current (f) and the Agulhas retroflection (e) display a signature in the magnitude of thermal gradients. The 12-year mean of SST gradients along the AC is persistently higher than 0.10◦ C/km with most values in the eastern side reaching 0.13◦ C/km. The mean magnitude of gradients appear to decay on both sides of the current, likely due to the current meandering about its mean position. The retroflection of the AC translates as large scattered values of horizontal gradients (e) whose 12-year mean ranges from 0.09 to 0.12◦ C/km. This scattering of high-gradient points extends approximately from 10 to 25◦ E and 39 to 45◦ S and is probably caused by the high temporal variability of rings and eddies forming westward of the AC as well as the eddy-shedding processes of the AR (Lutjeharms, 2006). • (g) The Benguela upwelling systems Fig. 2 clearly shows that maximum values of the 12-year mean of SST gradient magnitude in the SAO are concentrated in the western coast of the African continent. Mean values are systematically

higher than 0.11◦ C/km and reach up to 0.24◦ C/km in the southwestern side of Cape Peninsula and 0.18◦ C/km along the coasts of Namibia and Angola respectively. The intense frontal activity present in these regions is associated with the Benguela upwelling system, a series of equatorward and poleward coastal currents that extend from Cape Point in the southern tip of the African continent, up to the AngolaBenguela front near 17–18◦ S, where the Angola and the Benguela currents meet (Fennel, 1999; Small et al., 2015). Persistent alongshore winds due to land-sea thermal contrast (Nicholson, 2010) lead to significant coastal upwelling which in turn manifests as strong discontinuities in the spatial distribution of SST. 3.1. Seasonal cycle and intra-annual variability To investigate the annual and seasonal evolution of frontal activity in the selected regions of the SAO, we used monthly and seasonal composite maps of SST gradient magnitudes. For each sub-region, we only selected grid points inside the rectangular areas illustrated in Fig. 2 where the 12-year average exceeds 0.08◦ C/km to compute the seasonal and annual means of the magnitude of SST gradient. Therefore, in the Benguela region illustrated in Fig. 1g, only three areas were selected in our study. These are located off Cape Peninsula and off the coasts of Namibia and Angola. We also computed the coefficient of variation (CV) of the monthly mean of SST gradient magnitude to analyze the intra-annual variability. Results derived from the Terra and Aqua data are reported in Tables 1and 2. We first note a clear seasonality in frontal activity in the upwelling regions of Cape Frio, Cape Peninsula and the coasts of

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Fig. 5. Seasonal maps of SST gradient magnitude off the coast of Namibia.

Namibia and Angola. As can be seen from the CV reported in Tables 1 and 2, these regions exhibit the highest intra-annual variability among dynamic regions of the SAO. Further, the seasonal mean of SST gradients magnitude reaches its maximum and minimum during summer and winter respectively. This cycle is likely correlated to the seasonal variability of wind stress magnitude which decreases significantly in winter thus reducing the upwelling (Castelão et al., 2006). Another possible explanation is that during summer, the heating of coastal shallow waters by shortwave radiation is more efficient because the vertical redistribution of heat by mixing is depth-limited. Given the same heat flux, shallow waters reach a higher temperature, while deep waters have a deeper mixed layer and stronger advective fluxes. Offshore of a given isobath, the wind forcing pushes surface waters away, and the cool upwelled waters are in contact with the hot shallow waters at the frontal zone. This could be the mechanism leading to enhanced thermal gradients during summer. Seasonal maps of the Cape Frio area for example (Fig. 3) clearly show how spatial patterns related to strong frontal activity intensify from winter to summer, most notably near Cape Frio, Cape São Tomé and along the meander departing near the estuary of Rio Doce. In the vicinity of these three areas, an increase of ∼60% in the seasonal mean of SST gradient magnitudes occurs from winter to summer. Along the southwestern coast of Africa,

the region around Cape Peninsula displays similar season-to-season variations (Fig. 4) compared to Cape Frio. Maximum seasonal means (i.e., ≥0.20◦ C/km) that appear in summer are mainly distributed near the coast extending from Cape Point to Houth Bay (34◦ S) with lower values (i.e. between 0.12 and 0.17◦ C/km) near the southeastern and northwestern coast. Further north, in the upwelling coastal regions of Namibia and Angola, the seasonal cycle and intra-annual variability are less pronounced and show little spatial variability. The summer to winter decrease is of the order of 30% and for all seasons, the spatial distribution of frontal activity manifests as maximum seasonal means of SST gradient magnitudes peaking near the coast and gradually dissipating offshore (Figs. 5 and 6). The Brazil-Malvinas confluence region also displays a clear seasonal cycle with significant spatial variability of frontal activity (Fig. 7). Most notably, unlike upwelling regions, we observe an opposite cycle where the seasonal mean of SST gradient magnitude is maximum in winter, which is in agreement with Saraceno et al. (2005) This is due to the contrast in temperature between water masses of the Brazil Current and those flowing northward from the MC which happen to be significantly colder in winter, thus leading to sharper frontal structures. As shown in Fig. 7c, the strongest frontal activity in winter is concentrated in the northern part of the confluence zone, in an∼ 100 km wide band that extends from 34◦ S to

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Fig. 6. Seasonal maps of SST gradient magnitude off the coast of Angola.

37◦ S. This band almost fully dissipates in summer as the seasonal mean of SST gradient magnitude drops from 0.16◦ C/km (in winter) to values in the 0.06–0.09◦ C/km range. This reversed seasonality between upwelling areas and the BMC region is even more visible in the annual cycle of monthly mean of SST gradients magnitude illustrated in Fig. 8. A different seasonality can be identified south of 39◦ S where the two distinct bands associated with the MC appear. We observe that while the intensity of frontal activity over the two branches remains relatively unchanged from summer to fall, the magnitude of SST gradients in between them clearly decreases. The magnitude of SST gradients along these frontal structures reaches its minimum in winter and starts to amplify during spring. The seasonal cycle identified here is in agreement with the climatological monthly means of cross-shelf break SST gradients along the Patagonian shelf break reported in Franco et al. (2008) . Other selected sub-regions of the SAO, although showing frontal activity with the same intensity as in upwelling areas and in the BM confluence zone, exhibit weak seasonality and intra-annual variability. The CV of monthly means of SST gradient magnitude computed in the Agulhas current, the Agulhas retroflection, the Cape Horn current and at the merger between the SAF and the PF remains below 8%.

3.2. Interannual variability and long term trends To analyze the year-to-year variations and interannual variability of frontal activity over each region, annual means of SST gradient magnitudes were computed by averaging monthly means for each individual year for the 2003–2014 period. Annual means could be used for such analysis because monthly means of SST gradient magnitudes in selected regions follow a normal distribution. In Fig. 9, we plotted the annual average using Terra and Aqua data. Error bars in these graphs represent the sum of squared differences between Terra and Aqua monthly means used to calculate the annual mean. We also used the coefficient of variation (CV) computed for the 12-year annual mean time series to evaluate the interannual variability. The year-to-year variations observed in Fig. 9 are of relatively weak magnitude for all sub-regions of the SAO. In most cases, the absolute variation in the magnitude of frontal activity from one year to the next is well below 10%. We also note that in some cases, the annual mean remains significantly stable for extended periods of time. This can be seen off the coast of Angola for example, where the annual mean from 2010 to 2014 varies only by approximately ± 1%. In the Brazil-Malvinas region, the maximum variation from 2006 to 2010 is also only of the order of +2%. The values of the CV reported

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Fig. 7. Seasonal maps of SST gradient magnitude in the Brazil-Malvinas confluence region.

in Fig. 9 indicate that frontal activity in the selected areas of the SAO presents weak interannual variability with the strongest interannual variability in the SAO being observed off Cape Frio and off the coast of Namibia where CV values are 6.8% and 4.8% respectively. The interannual variability also appears to be independent of the magnitude of frontal activity in a given area. As an example, the Cape Peninsula region also exhibits very weak interannual variability (CV=2.5%) despite having the highest mean of SST gradient magnitude among other upwelling regions. In addition to year-to-year variations and interannual variability, we investigated long term changes in frontal activity for each region of the SAO using a 12-year time series of monthly means of SST gradient magnitude. We computed a best-fit linear regression with a 95% confidence level directly on the time series of monthly means of SST gradient magnitudes, without removing the annual cycle (Fig. 10). In fact, removal of the annual cycle resulted in time series of monthly anomalies from Terra and Aqua with a lower correlation, which indicates that errors in the estimation of monthly means of SST gradient magnitudes and the annual cycle are high enough to hinder the estimation of long term trends. It should be noted that for all selected regions, similarly to the monthly means of SST gradient magnitudes, the residual errors from the linear regression follow a Gaussian distribution. Trends reported for Terra and Aqua MODIS in Table 3 are expressed as percent of change from 2003 to 2014 using the slope

and intercept of the linear regression. Table 3 also reports the correlation between the time series computed from Terra and Aqua and shows significant consistency between the two sensors with respect to the estimation of thermal gradients. We first note that over some regions of the SAO namely Cape Peninsula and the coast of Angola, the sign of trends extracted from Terra and Aqua is in disagreement which indicates that the observed trends are not statistically significant and therefore unlikely to represent a long term change in frontal activity. A strong positive trend was identified over the Cape Frio upwelling region. Percents of change derived from Terra and Aqua are consistent and indicate that the magnitude of thermal fronts increased by approximately 16% since 2003. Cape Frio is the only region of the SAO where a significant positive trend was observed. In all other selected regions, the observed trends were negative with the strongest decline observed over the Cape Horn current and corresponding to a decrease in SST gradient magnitude of the order of −5 % from 2003 to 2014. The Brazil-Malvinas confluence region appears to be the most stable area of the SAO. The detected trends indicate that over the 12-year study period, the magnitude of SST gradients decreased approximately by only 1%. Slopes and intercepts computed using grid points over the entire SAO do not indicate any statistically significant trend given that the absolute percent of change in SST gradient magnitudes estimated from Terra and Aqua data is lower than 0.50 %.

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Fig. 8. Annual cycle of SST gradient magnitude in (a) Cape Frio (b) Cape Peninsula (c) off the coast of Namibia and (d) off the coast of Angola and (e) Brazil-Malvinas confluence region.

4. Conclusion We processed 12 years of full resolution satellite based SST observations from Terra ad Aqua MODIS to generate composite maps

of the magnitude of thermal gradients in the South Atlantic Ocean. Significant effort was dedicated to the improvement of data quality, namely stripe noise correction and improved cloud detection. Composite maps derived from original and processed data show

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Fig. 9. Annual means for each year over the 2003–2014 period of SST gradient magnitude over (a) Cape Frio (b) Cape Peninsula (c) Coast of Namibia (d) Coast of Angola and (e) Brazil-Malvinas confluence region.

major differences which underline the sensitivity of frontal studies to the quality of observations and the importance of mitigating quality issues prior to the generation of composite maps. Our analysis indicates that in most frontally active regions of the SAO, thermal

gradients have a clear seasonal cycle that varies depending on the nature of the processes involved in the formation and evolution of fronts. In upwelling coastal regions where thermal fronts are present throughout the year, SST gradients tend to reach maximum

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Fig. 10. 12-year time series (2003–2014) of SST gradient magnitude over (a) Cape Frio (b) Cape Peninsula (c) off the coast of Namibia and (d) off the coast of Angola and (e) Brazil-Malvinas confluence region.

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Table 3 Long term trend of SST gradient magnitude from Terra and Aqua MODIS. The last column of the table indicates the correlation between Terra and Aqua 12-year time series. –

Terra

Region

Slope

Cape Frio Cape Peninsula Namibia Coast Angola Coast Brazil-Malvinas Agulhas retroflection SAF ∩ PF Agulhas current Cape Horn Global SAO

Aqua Intercept −5

9.75e −3.07e −6 −1.92e −5 8.60e −6 −5.99e −6 −2.72e −5 −2.05e −5 −1.47e −5 −3.46e −5 8.90e −7

0.089 0.108 0.097 0.089 0.100 0.099 0.094 0.094 0.096 0.056

Trend 15.7 % −0.40 % −2.84 % 1.38 % −0.90 % −3.93 % −3.13 % −2.24 % −5.15 % 0.22 %

and minimum amplitudes during summer and winter respectively. Except for differences in magnitude, similar seasonal cycles were observed in both the region of Cape Frio and in the Benguela upwelling system. In regions where thermal fronts result from the interaction of large-scale flows, such as in the Brazil-Malvinas confluence region, the Agulhas retroflection zone and at the merger of the South Atlantic Front and the Antarctic Polar Front, we observed a different and less pronounced seasonal cycle where maximum and minimum frontal activity occurs late fall/winter and summer respectively. Annual means of SST gradient magnitudes computed from 2003 to 2014 show weak interannual variability in most regions of the SAO with coefficients of variation below 7%. Although not shown here, we didn’t observe any significant year-to-year variation in the spatial distribution of SST gradients. The analysis of the 12-year time series of SST gradient magnitude averaged over the whole SAO does not show any significant long term changes. In subregions of the SAO where the sign of slopes estimated from Terra and Aqua data are consistent, we observed trends that represent a decrease lower than 6% over the 2003–2014 period. The only significant trend was identified in the Cape Frio upwelling region where the magnitude of SST gradients increased by 16% in 12 years. It should be noted that the consistency of observations derived from Terra and Aqua MODIS, which translates as high spatial and temporal correlation in the derived composite maps and time series indicates that seasonal cycles and long term evolution identified in the SAO subregions are likely a reliable representation of its spatiotemporal properties. Nevertheless, additional data generated with an identical SST satellite retrieval algorithm, i.e., MCSST, and derived from an independent but similar sensor such as the Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) instrument could further confirm these observations and be used to statistically infer long term changes in the SAO thermal dynamics. Efforts to identify the primary factors behind the spatial distribution and temporal evolution of SST fronts in coastal and confluence regions will be conducted in a future study using altimetry, bathymetry and wind data.

Acknowledgments The authors would like to thank the four anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. Funding for this research was provided by the São Paulo Research Foundation FAPESP: 2013/183336 and 2008/58101-9.

References Acha, E.M., Mianzan, H.W., Guerrero, R.A., Favero, M., Bava, J., 2004. Marine fronts at the continental shelves of austral South America: physical and ecological processes. J. Mar. Syst. 44 (12), 83–105.

Correlation

Slope −4

1.05e 1.10e −5 −3.03e −5 −5.86e −6 −7.28e −6 −2.26e −5 −2.04e −5 −8.29e −6 −3.65e −5 −4.65e −7

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–

0.090 0.103 0.093 0.088 0.098 0.098 0.092 0.094 0.089 0.055

16.8 % 1.52 % −4.66 % −0.95 % −1.06 % −3.30 % −3.19 % −1.26 % −5.91 % −0.12 %

0.95 0.97 0.96 0.93 0.93 0.80 0.93 0.85 0.76 0.83

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