ALBERT

Description: The Arctic Ocean plays a key role in regulating the global climate, while being highly sensitive to climate change. Temperature in the Arctic increases faster than the global average, causing a loss of multiyear sea-ice and affecting marine ecosystem structure and functioning. As a result, Arctic primary production and biogeochemical cycling are changing. Here, we investigated inter-annual changes in the concentrations of particulate and dissolved organic carbon (POC, DOC) together with biological drivers, such as phyto- and bacterioplankton abundance in the Fram Strait, the Atlantic gateway to the Central Arctic Ocean. Data have been collected in summer at the Long-Term Ecological Research observatory HAUSGARTEN during eight cruises from 2009 to 2017. Our results suggest that the dynamic physical system of the Fram Strait induces strong heterogeneity of the ecosystem that displays considerable intra-seasonal as well as inter-annual variability. Over the observational period, DOC concentrations were significantly negatively related to temperature and salinity, suggesting that outflow of Central Arctic waters carrying a high DOC load is the main control of DOC concentration in this region. POC concentration was not linked to temperature or salinity but tightly related to phytoplankton biomass as estimated from chlorophyll-a concentrations (Chl-a). For the years 2009–2017, no temporal trends in the depth-integrated (0–100 m) amounts of DOC and Chl-a were observed. In contrast, depth-integrated (0–100 m) amounts of POC, as well as the ratio [POC]:[TOC], decreased significantly over time. This suggests a higher partitioning of organic carbon into the dissolved phase. Potential causes and consequences of the observed changes in organic carbon stocks for food-web structure and CO2 sequestration are discussed.

Description: Assessing the role of sea ice algal biomass and primary production for polar ecosystems remains challenging due to the strong spatio-temporal variability of sea ice algae. Therefore, the spatial representativeness of sea ice algal biomass and primary production sampling remains a key issue in large-scale models and climate change predictions of polar ecosystems. To address this issue, we presented two novel approaches to up-scale ice algal chl a biomass and net primary production (NPP) estimates based on profiles covering distances of 100 to 1,000 s of meters. This was accomplished by combining ice core-based methods with horizontal under-ice spectral radiation profiling conducted in the central Arctic Ocean during summer 2012. We conducted a multi-scale comparison of ice-core based ice algal chl a biomass with two profiling platforms: a remotely operated vehicle and surface and under ice trawl (SUIT). NPP estimates were compared between ice cores and remotely operated vehicle surveys. Our results showed that ice core-based estimates of ice algal chl a biomass and NPP do not representatively capture the spatial variability compared to the remotely operated vehicle-based estimates, implying considerable uncertainties for pan-Arctic estimates based on ice core observations alone. Grouping sea ice cores based on region or ice type improved the representativeness. With only a small sample size, however, a high risk of obtaining non-representative estimates remains. Sea ice algal chl a biomass estimates based on the dominant ice class alone showed a better agreement between ice core and remotely operated vehicle estimates. Grouping ice core measurements yielded no improvement in NPP estimates, highlighting the importance of accounting for the spatial variability of both the chl a biomass and bottom-ice light in order to get representative estimates. Profile-based measurements of ice algae chl a biomass identified sea ice ridges as an underappreciated component of the Arctic ecosystem because chl a biomass was significantly greater in this unique habitat. Sea ice ridges are not easily captured with ice coring methods and thus require more attention in future studies. Based on our results, we provide recommendations for designing an efficient and effective sea ice algal sampling program for the summer season.

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 satellite measurements. The multispectral-based PFT Chla retrievals are based on a revised version of the empirical OC-PFT algorithm applied to the Ocean Color Climate Change Initiative (OC-CCI) total Chla product. The PhytoDOAS analytical algorithm 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 PFT Chla data obtained from in situ marker pigment data and the NASA Ocean Biogeochemical Model simulations and satellite information on phytoplankton size. 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 goal of this study was to improve AS, which is a new retrieval method for quantitaidentification of major Phytoplankton Functional Types using hyper-spectral satellite data. PhytoDOAS is of the Differential Optical Absorption Spec(DOAS, a method for detection of atmospheric trace eloped for remote identification of oceanic phygroups. Thus far, PhytoDOAS has been sucxploited to identify cyanobacteria and diatoms global ocean from SCIAMACHY (SCanning ImagAbsorption spectroMeter for Atmospheric CartograpHY) -spectral data. The main challenge for retrieving more ytoDOAS is to overcome the correlation efbetween different PFTs’ absorption spectra. Differare composed of different types and amounts of ut also have pigments in common, e.g., chl-a, correlation effects in the usual performance of the retrieval. Two ideas have been implemented to ytoDOAS for the PFT retrieval of more phytogroups. Firstly, using the fourth-derivative specthe peak positions of the main pigment compoeach absorption spectrum have been derived. Afcomparing the corresponding results of major PFTs, the fit-window for the PhytoDOAS retrieval of each determined. Secondly, based on the results from spectroscopy, simultaneous fit of PhytoDOAS has proposed and tested for a selected set of PFTs (coccoldiatoms and dinoflagllates) within an optimized The method was then applied to the processSCIAMACHY data over the year 2005. Comparisons ytoDOAS PFT retrievals in 2005 with the moddata from the NASA Ocean Biochemical Model showed similar patterns in their seasonal distridiatoms and coccolithophores, especially in thenorthern parts of the global ocean. The seasonal patterns of the PhytoDOAS coccolithophores indicated very good agreement with the global distributions of Particulate Inorganic Carbon (PIC) provided by MODIS (MODerate resolution Imaging Spectroradiometer)-Aqua level-3 products. Since PIC is known as a proxy for the abundance of coccolithophores (in open ocean), the latter agreement indicates the basic functionality of the method in retrieving coccolithophores. Moreover, as a case study, the simultaneous mode of PhytoDOAS has been applied to SCIAMACHY data for detecting a coccolithophore bloom around New Zealand (reported by NASA from MODIS imagery in December 2009); the result was quite consistent with the MODIS RGB image and the MODIS PIC map of the bloom, indicating the functionality of the method in short-term retrievals.

Description: The relationship between phytoplankton assemblages and the associated optical properties of the water body is important for the further development of algorithms for large-scale remote sensing of phytoplankton biomass and the identification of phytoplankton functional types (PFTs), which are often representative for different biogeochemical export scenarios. Optical in-situ measurements aid in the identification of phytoplankton groups with differing pigment compositions and are widely used to validate remote sensing data. In this study we present results from an interdisciplinary cruise aboard the RV Polarstern along a north-to-south transect in the eastern Atlantic Ocean in November 2008. Phytoplankton community composition was identified using a broad set of in-situ measurements. Water samples from the surface and the depth of maximum chlorophyll concentration were analyzed by high performance liquid chromatography (HPLC), flow cytometry, spectrophotometry and microscopy. Simultaneously, the above- and underwater light field was measured by a set of high spectral resolution (hyperspectral) radiometers. An unsupervised cluster algorithm applied to the measured parameters allowed us to define bio-optical provinces, which we compared to ecological provinces proposed elsewhere in the literature. As could be expected, picophytoplankton was responsible for most of the variability of PFTs in the eastern Atlantic Ocean. Our bio-optical clusters agreed well with established provinces and thus can be used to classify areas of similar biogeography. This method has the potential to become an automated approach where satellite data could be used to identify shifting boundaries of established ecological provinces or to track exceptions from the rule to improve our understanding of the biogeochemical cycles in the ocean.

Description: The goal of this study was to improve PhytoDOAS, which is a new retrieval method for quantitative identification of major phytoplankton functional types (PFTs) using hyper-spectral satellite data. PhytoDOAS is an extension of the Differential Optical Absorption Spectroscopy (DOAS, a method for detection of atmospheric trace gases), developed for remote identification of oceanic phytoplankton groups. Thus far, PhytoDOAS has been successfully exploited to identify cyanobacteria and diatoms over the global ocean from SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) hyper-spectral data. This study aimed to improve PhytoDOAS for remote identification of coccolithophores, another functional group of phytoplankton. The main challenge for retrieving more PFTs by PhytoDOAS is to overcome the correlation effects between different PFT absorption spectra. Different PFTs are composed of different types and amounts of pigments, but also have pigments in common, e.g. chl a, causing correlation effects in the usual performance of the PhytoDOAS retrieval. Two ideas have been implemented to improve PhytoDOAS for the PFT retrieval of more phytoplankton groups. Firstly, using the fourthderivative spectroscopy, the peak positions of the main pigment components in each absorption spectrum have been derived. After comparing the corresponding results of major PFTs, the optimized fit-window for the PhytoDOAS retrieval of each PFT was determined. Secondly, based on the results from derivative spectroscopy, a simultaneous fit of PhytoDOAS has been proposed and tested for a selected set of PFTs (coccolithophores, diatoms and dinoflagellates) within an optimized fit-window, proven by spectral orthogonality tests. The method was then applied to the processing of SCIAMACHY data over the year 2005. Comparisons of the PhytoDOAS coccolithophore retrievals in 2005 with other coccolithophore-related data showed similar patterns in their seasonal distributions, especially in the North Atlantic and the Arctic Sea. The seasonal patterns of the PhytoDOAS coccolithophores indicated very good agreement with the coccolithophore modeled data from the NASA Ocean Biochemical Model (NOBM), as well as with the global distributions of particulate inorganic carbon (PIC), provided by MODIS (MODerate resolution Imaging Spectroradiometer)- Aqua level-3 products. Moreover, regarding the fact that coccolithophores belong to the group of haptophytes, the PhytoDOAS seasonal coccolithophores showed good agreement with the global distribution of haptophytes, derived from synoptic pigment relationships applied to SeaWiFS chl a. As a case study, the simultaneous mode of PhytoDOAS has been applied to SCIAMACHY data for detecting a coccolithophore bloom which was consistent with the MODIS RGB image and the MODIS PIC map of the bloom, indicating the functionality of the method also in short-term retrievals.

Description: Methyl iodide (CH3I), bromoform (CHBr3) and dibromomethane (CH2Br2), which are produced naturally in the oceans, take part in ozone chemistry both in the troposphere and the stratosphere. The significance of oceanic upwelling regions for emissions of these trace gases in the global context is still uncertain although they have been identified as important source regions. To better quantify the role of upwelling areas in current and future climate, this paper analyzes major factors that influenced halocarbon emissions from the tropical North East Atlantic including the Mauritanian upwelling during the DRIVE expedition. Diel and regional variability of oceanic and atmospheric CH3I, CHBr3 and CH2Br2 was determined along with biological and physical parameters at six 24 h-stations. Low oceanic concentrations of CH3I from 0.1–5.4 pmol L−1 were equally distributed throughout the investigation area. CHBr3 and CH2Br2 from 1.0 to 42.4 pmol L−1 and to 9.4 pmol L−1, respectively were measured with maximum concentrations close to the Mauritanian coast. Atmospheric CH3I, CHBr3, and CH2Br2 of up to 3.3, 8.9, and 3.1 ppt, respectively were detected above the upwelling, as well as up to 1.8, 12.8, and 2.2 ppt at the Cape Verdean coast. While diel variability in CH3I emissions could be mainly ascribed to oceanic non-biological production, no main driver was identified for its emissions over the entire study region. In contrast, biological parameters showed the greatest influence on the regional distribution of sea-to-air fluxes of bromocarbons. The diel impact of wind speed on bromocarbon emissions increased with decreasing distance to the coast. The height of the marine atmospheric boundary layer (MABL) influenced halocarbon emissions via its influence on atmospheric mixing ratios. Oceanic and atmospheric halocarbons correlated well in the study region, and in combination with high oceanic CH3I, CHBr3 and CH2Br2 concentrations, local hot spots of atmospheric halocarbons could solely be explained by marine sources. This conclusion is in contrast to previous studies that hypothesized elevated atmospheric halocarbons above the eastern tropical Atlantic to be mainly originated from the West-African continent.

Description: Carbon flow through pelagic food webs is an expression of the composition, biomass and activity of phytoplankton as primary producers. In the near future, severe environmental changes in the Arctic Ocean are expected to lead to modifications of phytoplankton communities. Here, we used a combination of linear inverse modeling and ecological network analysis to study changes in food webs before, during, and after an anomalous warm water event in the eastern Fram Strait of the West Spitsbergen Current (WSC) that resulted in a shift from diatoms to flagellates during the summer (June–July). The model predicts substantial differences in the pathways of carbon flow in diatom- vs. Phaeocystis/nanoflagellate-dominated phytoplankton communities, but relatively small differences in carbon export. The model suggests a change in the zooplankton community and activity through increasing microzooplankton abundance and the switching of meso- and macrozooplankton feeding from strict herbivory to omnivory, detritivory and coprophagy. When small cells and flagellates dominated, the phytoplankton carbon pathway through the food web was longer and the microbial loop more active. Furthermore, one step was added in the flow from phytoplankton to mesozooplankton, and phytoplankton carbon to higher trophic levels is available via detritus or microzooplankton. Model results highlight how specific changes in phytoplankton community composition, as expected in a climate change scenario, do not necessarily lead to a reduction in carbon export.