Comparison of Seasonal Cycles of Phytoplankton Chlorophyll, Aerosols, Winds and Sea-Surface Temperature off Somalia

Abstract

In climate research, an important task is to characterize the relationships between Essential Climate Variables (ECVs). Here, satellite-derived data sets have been used to examine the seasonal cycle of phytoplankton (chlorophyll concentration) in the waters off Somalia, and its relationship to aerosols, winds and Sea Surface Temperature (SST). Chlorophyll-a (Chl-a) concentration, Aerosol Optical Thickness (AOT), Ångström Exponent (AE), Dust Optical Thickness (DOT), SST and sea-surface wind data for a 16-year period were assembled from various sources. The data were used to explore whether there is evidence to show that dust aerosols enhance Chl-a concentration in the study area. The Cross Correlation Function (CCF) showed highest positive correlation (r 2 = 0.3) in the western Arabian Sea when AOT led Chl-a by 1-2 time steps (here, 1 time step is 8 days). A 2 × 2 • box off Somalia was selected for further investigations. The correlations of alongshore wind speed, Ekman Mass Transport (EMT) and SST with Chl-a were higher than that of AOT, for a lag of 8 days. When all four variables were considered together in a multiple linear regression, the increase in r 2 associated with the AOT is only about 0.02, a consequence of covariance among AOT, SST, EMT and alongshore wind speed. The AOT data show presence of dust aerosols most frequently during the summer monsoon season (June-September). When the analyses were repeated for the dust aerosol events, the correlations were generally lower, but still significant. Again, the inclusion of DOT in the multiple linear regression increased the correlation coefficient by only 2%, indicating minor enhancement in Chl-a concentration. Interestingly, during summer monsoon season, there is a higher probability of finding more instances of positive changes in Chl-a after one time step, regardless of whether there is dust aerosol or not. On the other hand, during the winter monsoon season (November-December) and rest of the year, the probability of Chl-a enhancement is higher when dust aerosol is present than when it is absent. The phase relationship in the 8-day climatologies of Chl-a and AOT (derived from NASA's SeaWiFS and MODIS-A ocean colour processing chain) showed that AOT Shafeeque et al.Seasonal Cycle of Phytoplankton Chlorophyll off Somalia led Chl-a for most of the summer monsoon season, except when Chl-a was very high, during which time, Chl-a led AOT. The phase shift in the Chl-a and AOT climatological relationship at the Chl-a peak was not observed when AOT from Aerosol Climate Change Initiative (Aerosol-CCI) was used.

The influence of tropical Indian Ocean warming and Indian Ocean Dipole on the surface chlorophyll concentration in the eastern Arabian Sea

“…The OC-CCI surface chlorophyll-a (Chl_a) concentration data from version 3.1 (Sathyendranath et al, 2018), for the period 1998 to 2014 has been used in this study. OC-CCI Chl_a data has already been verified and used to estimate the surface Chl_a concentration in the Arabian Sea by several authors (eg: Shafeeque et al, 2017;Monolisha et al, 2018;Smitha et al, 2019;Menon et al, 2019). SST data are based on extended reconstructed SST (ERSST v4) (Huang et al, 2015;Liu et al, 2015), produced on a 2°x2° grid derived from the International Comprehensive Ocean-Atmosphere Dataset (ICOADS).…”

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

“…They are also important players in the cycling of other elements in the ocean, including nitrogen, silica, and iron [3]. Phytoplankton are understood to impact atmospheric processes such as formation of aerosols through complex pathways [4,5,6,7]. Understanding phytoplankton is fundamental to anticipating how the marine ecosystem might respond to climate variability and climate change [8].…”

Long-Term Trends and Impact of SARS-CoV-2 COVID-19 Lockdown on the Primary Productivity of the North Indian Ocean

“…The correlation and lag/lead between the variables are estimated at 95 % confidence level. The phase relationships between the variables can help identify the underlying causes (Shafeeque et al, 2017) and therefore, we have used the phase relationships to find the correlation between the variables. The Chl-a is positively correlated with WS and NPP with correlations of 0.90 and 0.98 in AS1 and 0.66 and 0.97 in BB1.…”

“…The connection between phytoplankton bloom and dust deposition over Central AS was analyzed by Banerjee and Prasanna Kumar (2014) and found that blooms cannot be fully described by injection of nutrients by processes such as advection and mixing in the upper ocean. Shafeeque et al (2017) compared the seasonal cycles of phytoplankton with SST, Aerosol Optical Depth (AOD), and winds off Somalia. The productivity in the high-nutrient low chlorophyll (HNLC) regions of oceans is directly affected by the iron supply (Jickells et al, 2005).…”