1.1 Literature review
1.1.4 Variability in the Benguela upwelling: interannual variability, trends and decadal
1.1.4.1 Interannual variability of the Benguela Upwelling System
The Benguela Upwelling System also exhibits strong interannual and decadal variability (Hutchings et al., 2009). Like wind and ocean currents, the ocean temperature in the Benguela Upwelling System is influenced at interannual scale by ocean waves as well as the remote effect of climate modes such as El Niño Southern Oscillation (ENSO) (Rouault et al., 2010; Dufois and Rouault, 2012; Blamey et al., 2015). The Northern Benguela is under the influence of the Benguela Niño, and Benguela Niña phenomena linked to the propagation of coastal trapped waves triggered remotely in the Equatorial region (Shannon et al., 1986; Rouault et al., 2007;
Lübbecke et al., 2010; Bachèlery et al., 2016; Rouault et al., 2018; Bachèlery et al., 2020; Illig et al., 2020). Coastal trapped waves induce poleward advection of warm tropical water to the Northern Benguela. These waves are responsible for about 80% of the Sea Surface Height (SSH) anomalies (Bachèlery et al., 2016) off Angola and Northern Namibia. Coastal trapped waves deepen or shallow the thermocline and favour the Benguela Niño warm events and Benguela
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Niña cold events, respectively. The interannual variability of Benguela Niños/Niñas has been intensively studied in the region (Florenchie et al., 2003; Florenchie et al., 2004; Rouault et al., 2007; Lübbecke et al., 2010; Bachèlery et al., 2016; Rouault et al., 2018; Imbol Koungue et al., 2019; Bachèlery et al., 2020) and has been found as the main mode of interannual variability of SST (e.g., Florenchie et al., 2003; Rouault et al., 2018) in the Northern Benguela and Southern Angola. The Benguela Niños/Niñas have no impact on the Central and South Benguela. However, some studies that have been done in these regions reveal that the Benguela is impacted by large- scale climate modes such as ENSO (Agenbag, 1996; Rouault et al., 2010; Dufois and Rouault, 2012; Blamey et al., 2015). Rouault et al. (2010) found that the correlation between SST in Southern Benguela and the Southern Annular Mode (SAM) is not coherent. The ENSO impact the Southern Benguela upwelling by modulating the latitudinal variation of SASH (Dufois and Rouault, 2012; Sun et al., 2017).
1.1.4.2 Trends and Decadal Variability
Reduced equatorward coastal wind decreases the upwelling rate and limits nutrient enrichment of the euphotic zone with negative impacts on primary production, while stronger upwelling conditions may increase the nutrient supply and at the same time increase the offshore transport (Bakun et al., 2010; Bakun et al., 2015; García-Reyes et al., 2015). Accordingly, understanding long-term trends in the Benguela Upwelling System, which is part of my thesis objective, is of great importance for the ecological functioning, resources productivity, and environmental health of the Benguela Upwelling System. Under global climate warming, Bakun (1990) proposed that an increase in coastal upwelling-favourable winds would lead to the intensification of the continental-oceanic pressure gradient and increase upwelling rate. Several studies have identified trends in upwelling in the Benguela region (Table 1.1). Some support Bakun hypothesis and some contradict it. Most of these studies are focused on wind analysis.
Narayan et al. (2010) found a positive trend in meridional wind stress at Luderitz over 1960-2000 using International Comprehensive Ocean-Atmosphere Data Set (ICOADS) wind data. Sydeman et al. (2014) found positive trends in the South of Benguela Upwelling System, over the last 60 years, in several wind data sets. Lamont et al. (2018), using synoptic-scale upwelling indices based
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on Ekman transport derived from National Centers for Environmental Prediction version 2 (NCEP2) reanalysis dataset over the period 1979 to 2015, found a weakening of the upwelling in the Northern Benguela and a strengthening in the Southern Benguela. In contrast, Narayan et al.
(2010) study found no trend over the period 1960-2000 when using ERA 40 surface wind speed reanalysis dataset. Few studies focus on ocean SST, a good indicator of the upwelling strength.
There is also substantial but conflicting evidence of negative trends in SST (García-Reyes et al., 2015). A negative trend in SST was found by Narayan et al. (2010) and Santos et al. (2012) over the period 1960-2009 using HadISST. However, a positive trend in SST was observed with the same datasets over the period 1870-2006. Rouault et al. (2010) updated by Blamey et al. (2015) found that the upwelling in the Northern Benguela shows a positive trend in SST and the upwelling in South Benguela shows a negative trend while the Central Benguela has not significantly changed. Vizy and Cook (2016) found a positive trend along the Namibian coast in several reanalysis datasets. Recently, using a coupled ocean-atmospheric model, Vizy et al.
(2018) found a negative trend in mixed layer temperature in the Northern Benguela upwelling.
However, caution is required when extracting trends from a climate model (Vizy et al., 2018), particularly in regions like tropical Atlantic, where these models are known to produce a mean warm bias (Richter et al., 2012; Toniazzo and Woolnough, 2014; Exarchou et al., 2018; Koseki et al., 2018). Abrahams et al. (2021) using a series novel of metrics related to SST from OISST (¼°×
¼°) and upwelling index based on ERA5 wind speed and direction found that in Southern Benguela, an increase of the upwelling favorable winds is associated with a decrease of the SST while in the Northern Benguela an increase of upwelling favorable wind shows an increase of SST.
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Table 1.1: Upwelling trends in the Benguela upwelling system
Domaine Author(s) Data used Variable Period of
study
Trend results
Northern Benguela
Lamont et al., (2018) NCEP2 UI based on Ek transport
1979-2015 negative
Rouault et al. 2010 OISST (1° x 1°) SST 1982-2009 positive Blamey et al.,2015 OISST (1° x 1°) SST 1982-2012 positive Vizy and Cook (2016) OISST, Era-Int,
NCEP2 MERRA
SST 1982-2013 positive
Vizy et al. (2018) CRCM model Mixed layer Temperature
1980-2014 negative
Abrahams et al., 2021
OISST (¼° x ¼°) and ERA5
Metric based on SST and UI estimated from wind speed and direction
1982-2018 Positive in UI and SST
Central Benguela
Nararyan et al., (2010)
ICOADS Wind stress 1906-2000 positive
Nararyan et al., (2010)
ERA-40 Wind speed 1906-2000 No trend
Nararyan et al., (2010)
HadISST SST 1970-2006 negative
Santos et al., (2012) HadiSST SST 1960-2009 negative Rouault et al. 2010 OISST (1° x 1°) SST 1982-2009 No trend
Sideman et al., (2014)
Several datasets
Wind Up to last 60 years
positive
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Benguela
Lamont et al., (2018) NCEP2 UI based on Ek transport
1979-2015 positive
Rouault et al. 2010 OISST (1° x 1°) SST 1982-2009 Negative Blamey et al., 205 OISST (1° x 1°) SST 1982-2012 negative Santos et al., (2012) HadiSST SST 1960-2009 negative
Abrahams et al., 2021
OISST (¼° x ¼°) and ERA5
Metric based on SST and UI estimated from wind speed and direction
1982-2018 Positive in UI and Negative in
SST
The failure of consensus among studies could be due to varying spatio-temporal resolutions of the datasets (García-Reyes et al., 2015) and the method used to construct the datasets (Blamey et al., 2015). For example, SST data with coarse resolution cannot distinguish nearshore upwelling related temperatures from offshore temperatures. Due to the spatial and temporal availability of data, most of the climate datasets are reconstructed using different types of in-situ and satellite data. That could create an artificial trend in datasets. In the Benguela coastal area, Smit et al. (2013) reported large biases of up to 6°C in places between satellite-derived and in-situ climatological temperatures. Moreover, Blamey et al. (2015) indicate serious problems in the validity of the SST datasets for trend studies in the Benguela Upwelling System. Decadal variability could be also another factor explaining the temporal change in the trend. In the Benguela Upwelling System, to our knowledge, there are few or no works on the decadal variability of SST. Only Hutchings et al. (2009) mentioned decadal variability in SST anomalies from a combination of ICOADS and Satellite data in Northern Benguela. However, several studies have identified decadal variability in the southern African climate system, especially in South African rainfall (Dyer and Tyson, 1977; Jury, 2015; Dieppois et al., 2016). In their study, Dieppois et al. (2016), have demonstrated the existence of two significant signals of decadal variability, including an interdecadal and a quasi-decadal period (15-28 years, and 8-13 years respectively)
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in southern Africa rainfall linked to Pacific Ocean climate modes such as the Pacific Decadal Oscillation (PDO), the Interdecadal Pacific Oscillation (IPO) and ENSO in both the austral summer rainfall region and the austral winter rainfall region which is where the South Benguela is situated. Furthermore, there are potential links between the region’s rainfall and west and south coast of Southern Africa (Wolski et al., 2021).