Amplified Arctic Surface Warming and Sea Ice Loss Due to Phytoplankton and Colored Dissolved Material

Optically active water constituents attenuate solar radiation and hence affect the vertical distribution of energy in the upper ocean. To understand their implications, we operate an ocean biogeochemical model coupled to a general circulation model with sea ice. Incorporating the effect of phytoplankton and colored dissolved organic matter (CDOM) on light attenuation in the model increases the sea surface temperature in summer and decreases sea ice concentration in the Arctic Ocean. Locally, the sea ice season is reduced by up to one month. CDOM drives a significant part of these changes, suggesting that an increase of this material will amplify the observed Arctic surface warming through its direct thermal effect. Indirectly, changing advective processes in the Nordic Seas may further intensify this effect. Our results emphasize the phytoplankton and CDOM feedbacks on the Arctic ocean and sea ice system and underline the need to consider these effects in future modeling studies to enhance their plausibility

SynSenPFT: ein globaler Datensatz zur Verteilung von funktionalen Gruppen von Phytoplankton im Ozean.

Eine Studie der Arbeitsgruppe „Phytooptics“ am AWI, die in Zusammenarbeit mit dem Institut für Umweltphysik der Universität Bremen (IUP-UB), dem „Laboratoire d’Océanographie de Villefranche (LOV, Villefranche, Frankreichce) und dems „Plymouth Marine Laboratory (PML, Plymouth, UKnited Kingdom) durchgeführterstellt wurde, entwickelte eine Methodeschlägt nun vor, wie man, die diese oben genannten Mängel der gegenwärtigen multispektralen PFT Produkte (Bereitstellen von entweder nur dominanten Phytoplanktongruppen oder Datenprodukte mit einer besonderen Koppelung an a priori Information, Bracher et al. 2017a?) und dervon gegenwärtigen Phyto DOAS-Datenprodukten (niedrige zeitliche und räumliche Abdeckung) beheben kann. Die Autoren kombiniertenuntersuchten eine Möglichkeit, um die hyperspektralen Daten, welche durch eine hohe spektrale und eine grobe räumliche Auflösung charakterisiert sind, mit multispektralen Daten, die eine höhere räumliche und zeitliche Auflösung besitzen, zu verknüpfen (SynSenPFT, Losa et al. 2017a?). Der SynSenPFT Algorithmus (Abbildung 1) basiert auf den ursprünglichen Eingabedaten von überarbeitetern Versionen (Bracher et al. 2017b, Losa et al. 2017a) dervon existierenden PFT Algorithmen, die auf hyper- und multispektralen Informationen basieren – PhytoDOAS (Bracher et al. 2009, Sadeghi et al. 2012, Bracher et al. 2017?) und OC-PFT (Hirata et al. 2011, Soppa et al. 2014). Durch eine synergistische Verknüpfung mittels optimaler Interpolation leitet der neue Algorithmus PFT Produkte mit zeitlicher und räumlicher Auflösung von multispektralen „ocean colour“-Daten ab, allerdings basierend auf der Nutzung der spektralen Informationen der hyperspektralen Daten. Die Grundzüge des Algorithmus, die die größte Herausforderung bei der späteren Implementierung darstellen, zusammen mit die Sensitivitätsstudien und die Evaluierung anhand eines großen globalen in-situ PFTs Chla-Datensatzes (Soppa et al. 2017), wurden in Losa et al. 2017a? veröffentlicht. In der Veröffentlichung heben die Autoren die Perspektiven des SynSenPFT-Systems für zukünftige Anwendungen hervor, bezogen auf die hyperspektralen Sensoren Sentinel-5-Precursor, Sentinel-4 und Sentinel-5 und dem multispektralen Sensor OLCI auf Sentinel-3. Ziel dabei ist die weiter verbesserte räumliche Auflösung des erhaltenen PFT Chla Produktes und die Verlängerung der Zeitserien über die nächsten Dekaden mit Hilfe der Sentinel-Missionen

The HBM-PDAF assimilation system for operational forecasts in the North and Baltic Seas

The HIROMB-BOOS Model (HBM) has been coupled with the Parallel Data Assimilation Framework PDAF (http://pdaf.awi.de) in order to improve hydrographic forecasts in the North and Baltic Seas. The coupled forecast system assimilates satellite sea surface temperature as well as in situ data of temperature and salinity profiles to initialize forecasts up to 5 days. The assimilation uses an ensemble Kalman filter, which dynamically estimates the uncertainty of the state estimate with an ensemble of model states and applies spatially localized updates to improve the ocean state. The structure of the assimilation system, which can analogously be used to extend other forecast models for data assimilation, is discussed. Applying the assimilation reduces errors of the surface temperature by about 0.2 degrees C

Antarctic phytoplankton in response to environmental changes studied by a synergistic approach using multi- and hyper-spectral satellite data (PhySyn)

The project focuses on the assessment of the impact of environmental change in the Southern Ocean on phytoplankton. Phytoplankton is the key organism determining the functioning of the marine ecosystem and biogeochemical cycle and it can be detected from space. In this study analytical bio-optical retrieval techniques are to be used to develop generic methods, which extract unique global long-term information on phytoplankton composition. The methods will be based on using all available high-resolution optical satellite data which are complemented by in-situ and multi-spectral satellite data. Combined with modeling studies, this information will be used to attribute the relative importance of anthropogenic activity and natural phenomena on the marine ecosystem and biogeochemical cycling of the Southern Oceans during the last decades

Phytoplankton functional types observation from space in the Fram Strait (2002-2020)

Phytoplankton in the sunlit layer of the ocean act as the base of the marine food web fueling fisheries, and also regulate key biogeochemical processes. Phytoplankton composition structure varies in ocean biomes and different phytoplankton groups drive differently the marine ecosystem and biogeochemical processes. Because of this, variations in phytoplankton composition influence the entire ocean environment, specifically the ocean energy transfer and the export of organic carbon to the deep ocean. As one of the algorithms deriving phytoplankton composition from space borne data, within the framework of the EU Copernicus Marine Service (CMEMS), EOF-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021; https://marine.copernicus.eu/). By using multi-sensor merged products and Sentinel-3 OLCI data, the algorithm provides global chlorophyll a data with per-pixel uncertainty for diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today. Due to different lifespans and radiometric characteristics of the ocean color sensors, the consistency of the PFTs is evaluated to provide quality-assured data for a consistent long-term monitoring of the phytoplankton community structure. As current commonly used phytoplankton carbon estimation methods rely mostly on the backscattering property of phytoplankton, which could vary dramatically for different phytoplankton taxa, as a perspective of this study, phytoplankton carbon may be better estimated in a way that accounts for phytoplankton taxonomy

20-year satellite observations of phytoplankton functional types (PFTs) in the Atlantic Ocean

Phytoplankton composition structure varies in ocean biomes. Different phytoplankton groups drive differently the marine ecosystem and biogeochemical processes. Therefore, variations in phytoplankton composition influence the entire ocean environment, specifically the ocean energy transfer, the deep ocean carbon export, water quality etc. As one of the algorithms deriving phytoplankton composition from space borne data, the EOF-PFT algorithm was developed using multi-spectral satellite data collocated to an extensive global in-situ PFT data set based on HPLC pigments and sea surface temperature data (Xi et al. 2020, 2021). By using multi-sensor merged products and Sentinel-3 OLCI data, the algorithm provides global chlorophyll a (Chla) data with per-pixel uncertainty for diatoms, haptophytes, dinoflagellates, chlorophytes and prokaryotic phytoplankton spanning the period from 2002 until today, with products available on the EU Copernicus Marine Service (CMEMS). The objectives of this study are to 1) evaluate CMEMS PFT products and improve their continuity along the products derived from different satellite sensors, and 2) 20-year satellite PFT products for time series analysis of climatology, trends, anomaly and phenology of multiple PFTs in the whole Atlantic and its different biogeochemical provinces (Longhurst, 2006)

Taking into account atmospheric uncertainty improves sequential assimilation of SMOS sea ice thickness data in an ice-ocean model

The sensitivity of assimilating sea ice thickness data to uncertainty in atmospheric forcing fields is examined using ensemble-based data assimilation experiments with the Massachusetts Institute of Technology General Circulation Model (MITgcm) in the Arctic Ocean during November 2011–January 2012 and the Met Office (UKMO) ensemble atmospheric forecasts. The assimilation system is based on a local singular evolutive interpolated Kalman (LSEIK) filter. It combines sea ice thickness data derived from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) satellite and Special Sensor Microwave Imager/Sounder (SSMIS) sea ice concentration data with the numerical model. The effect of representing atmospheric uncertainty implicit in the ensemble forcing is assessed by three different assimilation experiments. The first two experiments use a single deterministic forcing dataset and a different forgetting factor to inflate the ensemble spread. The third experiment uses 23 members of the UKMO atmospheric ensemble prediction system. It avoids additional ensemble inflation and is hence easier to implement. As expected, the model-data misfits are substantially reduced in all three experiments, but with the ensemble forcing the errors in the forecasts of sea ice concentration and thickness are smaller compared to the experiments with deterministic forcing. This is most likely because the ensemble forcing results in a more plausible spread of the model state ensemble, which represents model uncertainty and produces a better forecast

Global retrieval of phytoplankton functional types based on empirical orthogonal functions using CMEMS GlobColour merged products and further extension to OLCI data

This study presents an algorithm for globally retrieving chlorophyll a (Chl-a) concentrations of phytoplankton functional types (PFTs) from multi-sensor merged ocean color (OC) products or Sentinel-3A (S3) Ocean and Land Color Instrument (OLCI) data from the GlobColour archive in the frame of the Copernicus Marine Environmental Monitoring Service (CMEMS). The retrieved PFTs include diatoms, haptophytes, dinoflagellates, green algae and prokaryotic phytoplankton. A previously proposed method to retrieve various phytoplankton pigments, based on empirical orthogonal functions (EOF), is investigated and adapted to retrieve Chl-a concentrations of multiple PFTs using extensive global data sets of in situ pigment measurements and matchups with satellite OC products. The performance of the EOF-based approach is assessed and cross-validated statistically. The retrieved PFTs are compared with those derived from diagnostic pigment analysis (DPA) based on in situ pigment measurements. Results show that the approach predicts well Chl-a concentrations of most of the mentioned PFTs. The performance of the approach is, however, less accurate for prokaryotes, possibly due to their general low variability and small concentration range resulting in a weak signal which is extracted from the reflectance data and corresponding EOF modes. As a demonstration of the approach utilization, the EOF-based fitted models based on satellite reflectance products at nine bands are applied to the monthly GlobColour merged products. Climatological characteristics of the PFTs are also evaluated based on ten years of merged products (2002−2012) through inter-comparisons with other existing satellite derived products on phytoplankton composition including phytoplankton size class (PSC), SynSenPFT, OC-PFT and PHYSAT. Inter-comparisons indicate that most PFTs retrieved by our study agree well with previous corresponding PFT/PSC products, except that prokaryotes show higher Chl-a concentration in low latitudes. PFT dominance derived from our products is in general well consistent with the PHYSAT product. A preliminary experiment of the retrieval algorithm using eleven OLCI bands is applied to monthly OLCI products, showing comparable PFT distributions with those from the merged products, though the matchup data for OLCI are limited both in number and coverage. This study is to ultimately deliver satellite global PFT products for long-term continuous observation, which will be updated timely with upcoming OC data, for a comprehensive understanding of the variability of phytoplankton composition structure at a global or regional scale