ALBERT

Description: The Antarctic Circumpolar Current has a high potential for primary production and carbon sequestration through the biological pump. In the current study, two large-scale blooms observed in 2012 during a cruise with R.V. Polarstern were investigated with respect to phytoplankton standing stocks, primary productivity and nutrient budgets. While net primary productivity was similar in both blooms, chlorophyll a -specific photosynthesis was more efficient in the bloom closer to the island of South Georgia (39 °W, 50 °S) compared to the open ocean bloom further east (12 °W, 51 °S). We did not find evidence for light being the driver of bloom dynamics as chlorophyll standing stocks up to 165 mg/m² developed despite mixed layers as deep as 90 m. Since the two bloom regions differ in their distance to shelf areas, potential sources of iron vary. Nutrient (nitrate, phosphate, silicate) deficits were similar in both areas despite different bloom ages, but their ratios indicated more pronounced iron limitation at 12 °W compared to 39 °W. While primarily the supply of iron and not the availability of light seemed to control onset and duration of the blooms, higher grazing pressure could have exerted a stronger control toward the declining phase of the blooms.

Description: Phytoplankton community structure and their physiological response in the vicinity of the Antarctic Polar Front (APF; 44°S to 53°S, centred at 10°E) were investigated as part of the ANT-XXVIII/3 Eddy-Pump cruise conducted in austral summer 2012. Our results show that under iron-limited (〈 0.3 µmol/m**3) conditions, high total chlorophyll-a (TChl-a) concentrations (〉 0.6 mg/m**3) can be observed at stations with deep mixed layer (〉 60 m) across the APF. In contrast, light was excessive at stations with shallower mixed layer and phytoplankton were producing higher amounts of photoprotective pigments, diadinoxanthin (DD) and diatoxanthin (DT), at the expense of TChl-a, resulting in higher ratios of (DD+DT)/ TChl-a. North of the APF, significantly lower silicic acid (Si(OH)4) concentrations (〈 2 mmol/m**3) lead to the domination of nanophytoplankton consisting mostly of haptophytes, which produced higher ratios of (DD+DT)/TChl-a under relatively low irradiance conditions. The Si(OH)4 replete (〉 5 mmol/m**3) region south of the APF, on the contrary, was dominated by microphytoplankton (diatoms and dinoflagellates) with lower ratios of (DD+DT)/TChl-a, despite having been exposed to higher levels of irradiance. The significant correlation between nanophytoplankton and (DD+DT)/TChl-a indicates that differences in taxon-specific response to light are also influencing TChl-a concentration in the APF during summer. Our results reveal that provided mixing is deep and Si(OH)4 is replete, TChl-a concentrations higher than 0.6 mg/m**3 are achievable in the iron-limited APF waters during summer.

Description: We derive the chlorophyll a concentration (Chla)for three main phytoplankton functional types (PFTs)-- diatoms, coccolithophores and cyanobacteria- by combining satellite multispectral-based information, being of a high spatial and temporal resolution, with retrievals based on high resolution of PFT absorption properties derived from hyperspectral measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm (Hirata et al. 2011) applied to the Ocean Colour Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm (Bracher et al. 2009, Sadeghi et al. 2012) is used with some modifications to derive PFT Chla from SCIAMACHY hyperspectral measurements. To combine synergistically these two PFT products (OC-PFT and PhytoDOAS), an optimal interpolation is performed for each PFT in every OC-PFT sub-pixel within a PhytoDOAS pixel, given its Chla and its a priori error statistics. The synergistic product (SynSenPFT) is presented for the period of August 2002 ? March 2012 and evaluated against in situ HPLC pigment data and satellite information on phytoplankton size classes (PSC) (Brewin et al. 2010, Brewin et al. 2015) and the size fraction (Sf) by Ciotti and Bricaud (2006. The most challenging aspects of the SynSenPFT algorithm implementation are discussed. Perspectives on SynSenPFT product improvements and prolongation of the time series over the next decades by adaptation to Sentinel multi- and hyperspectral instruments are highlighted.

Description: The SynSenPFT product is presented as chlorophyll "a" concentrations (Chla) for diatoms, coccolithophores and cyanobacteria (some of the phytoplankton functional types, PFT) obtained globally over the World Ocean on a 4 km sinusoidal grid on a daily basis over the period of August 2002 - March 2012. The SynSenPFT is a synergistic combination of the PFT products of initial-input OC-PFT (Hirata et al., 2011, Soppa et al., 2014) applied to total chlorophyll "a" (TChla) data of Ocean Colour Climate Change Initiative (OC-CCI, Version 2, ESA) and PhytoDOAS (Bracher et al., 2009, Sadeghi et al., 2012) version 3.3 available at doi:10.1594/PANGAEA.870486 with an optimal interpolation (OI). The OI method is applied to OC-PFT and PhytoDOAS Chla products of diatoms, cyanobacteria (called prokarytoes by the OC-PFT method) and haptophytes (for OC-PFT) and coccolithophores (for PhytoDOAS). Note that OC-PFT retrieves haptophytes while PhytoDOAS retrieves coccolithophores, a (often dominating) sub-group of haptophytes. Algorithmically, the SynSenPFT is an update of OC-PFT Chla with PhytoDOAS Chla values weighted in accordance to our degree of belief to both initial-input data products. Within the current version of SynSenPFT algorithm the update is done for every sub-pixel of OC-PFT within a PhytoDOAS pixel. Thus, SynSenPFT in every OC-PFT sub-pixel on average is nudged towards PhytoDOAS values as close as allowed by the prescribed PhytoDOAS and OC-PFT error statistics.

Description: Chlorophyll "a" concentrations (Chla) for diatoms, haptophytes and prokaryotes have been derived using the Diagnostic Pigment Analysis by Vidussi et al. (2001) and Uitz et al. (2006) modified as in Hirata et al. (2011) and Brewin et al. (2015) of in situ HPLC phytoplankton pigment data compiled from several databases and individual cruises. The weights used in the analysis were revised in accordance to this enlarged in situ pigment data set. The in situ database includes databases French campaigns (Atalante-3, KEOPS and Bonus Good Hope) and German research vessels (RV) Maria S. Merian, Meteor, Polarstern, Poseidon and Sonne. Cruises with RV Polarstern to the Southern hemisphere are labeled as ANT, to the Northern hemisphere as ARK, with RV Poseidon as POS, with RV Maria S. Merian with MSM, with RV Meteor as M and with RV Sonne as SO. A campaign in Indonesian waters was conducted from a small fisher boat.

Description: This phytoplankton group (PFT) concentration a (Chl a) data are output from the algorithm PhytoDOAS version 3.3 applied to SCIAMACHY data from 2 Aug 2002 to 8 Apr 2012. Data have been gridded monthly on 0.5° latitude to 0.5°. For cyanobacteria (includes all prokaryotic phytoplankton) and diatoms the PhytoDOAS PFT retrieval algorithm by Bracher et al. (2009) and for coccolithophores the algorithm by Sadeghi et al. (2012) have been used. However, these methods have slightly been improved which includes: - Data during SCIAMACHY instrument decontamination are excluded in the analysis. - SCIAMACHY level-1b input data for PhytoDOAS are now version 7.04 data (instead of version 6.0). - The wavelength window for all three phytoplankton groups (PFTs) fit factor starts at 427.5 nm (instead of 429 nm). - Coccolithophores fit factors are retrieved in a retrieval fitting simultaneously diatoms and coccolithophores (instead of a triple fit with also fitting dinoflagellates as in Sadeghi et al. 2012). - Vibrational Raman Scattering (VRS) is now fitted directly in the blue spectrum (450 to 495 nm), following Dinter et al. (2015), (instead of in the UV—A region as in Vountas et al. 2007) except that here the daily solar background spectrum measured by SCIAMACHY and the VRS pseudo absorption spectrum calculated based on a SCIAMACHY solar spectrum following Vountas et al. (2007) was used in order to correct for the variation of instrumental effects over time (this is not achieved when using the RTM simulated background spectrum as done in Dinter et al. 2015). - The PFT Chl a are derived from the ratio of the PFT fit factor to the VRS fit factor multiplied by a LUT (Look Up Table). The LUT is based on radiative transfer model (RTM) SCIATRAN simulations (see Rozanov et al. 2014) accounting also for changing solar zenith angle (SZA).