Ocean Color 2

The importance of phytoplankton blooms for the Arctic marine ecosystem is well recognized but studies disagree as to the consequences of sea ice melt on the phytoplankton distribution and growth. This limited understanding of actual and future Arctic phytoplankton dynamics mostly results from a lack of accurate data from the receding ice-edges where phytoplankton blooms are known to occur. Ocean color sensors on board satellites therefore represent a crucial tool for providing a synoptic view of the ocean systems over broad spatio-temporal scales. However, today the use of ocean color data in Arctic environments remains strongly compromised due to sea ice contamination, among other things. Indeed, medium ocean color data along the receding ice edge are “dazzled” by nearby and/or sub-pixel highly reflective ice floes. Standard ocean color data methods ignore ice-contamination during data processing, which deteriorates the quality of the radiometric data and subsequent satellite derived bio-geochemical products. Moreover, since Arctic phytoplankton spring blooms typically develop along receding ice-edges, ignoring ice-contaminated pixels may lead to misinterpretation of satellite data. The present study shows how adjacent and sub-pixel sea-ice floes affect the retrieved ocean color data. A correction approach is also suggested to improve the “dazzled” ocean color pixels along the receding ice edge with the aim of advancing our understanding of current and future trends in phytoplankton dynamics. The advantage of a synergy between high and medium spatial resolution to correct ocean color data from sea-ice contamination is also illustrated. This work is funded by the ESA Living Planet Fellowship.

Given the recent strong international focus on developing new data assimilation systems for biological models, we present in this comparative study the application of newly developed state-parameters estimation tools to an ocean ecosystem model. It is quite known that the available physical models are still too simple compared to the complexity of the ocean biology. Furthermore, various biological parameters remain poorly unknown and hence wrong specifications of such parameters can lead to large model errors.

Standard joint state-parameters augmentation technique using the ensemble Kalman filter (Stochastic EnKF) has been extensively tested in many geophysical applications. Some of these assimilation studies reported that jointly updating the state and the parameters might introduce significant inconsistency especially for strongly nonlinear models. This is usually the case for ecosystem models particularly during the period of the spring bloom. A better handling of the estimation problem often consists of separating the update of the state and the parameters using the so-called Dual EnKF. The dual filter is computationally more expensive than the Joint EnKF but is expected to perform more accurately. Using a similar separation strategy, we propose a new EnKF estimation algorithm in which we apply a one-step-ahead smoothing to the state. We further perform the update step after transforming the state variables and parameters to a Gaussian space using different anamorphosis formulations. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. Unlike the classical filtering path, the new scheme starts with an update step and later a model propagation step is performed.   

We test the performance of the new smoothing-based schemes against the standard EnKF in a one-dimensional configuration of the GOTM-NORWECOM system in the North Atlantic. We use nutrients profile (up to 2000 m deep) data and surface chlorophyll-a measurements from Mike weather station (66^o N, 2^o E) to estimate different biological parameters of phytoplanktons and zooplanktons. We analyze the performance of the filters in terms of complexity and accuracy of the state and parameters estimates.

Global climate models that include explicit description of the marine Carbon cycle are a standard tool for long term predictions of the global climate change. Increased detail in the description of the marine Carbon cycle imply a corresponding need for direct Carbon measurements to guide evaluation of the models. Thus the existing satellite algorithms capable of retrieving Carbon from the ocean colour signal need to be assessed. On the other hand,  development of satellite algorithms to derive Carbon concentrations in the ocean benefit from application of concepts already in use in dynamic ocean models.

The ESA SEOM project POCO (Pools of Carbon in the Ocean) is designed to examine, evaluate, improve and implement algorithms for estimating pools of carbon in the ocean from satellite data, and to evaluate their readiness for use in climate-change studies. The project also compares methods used by ecosystem modellers to convert between carbon and chlorophyll, to assess whether any of these approaches can be adapted for application in remote sensing.

In the first phase of the project, much effort has been dedicated to capture the needs of the users to define the characteristics of the products to be generated. This is a two way process as it has also informed users from the modelling community about the potential and the limitations of Carbon products derived from ocean colour. During this interaction, it has been found that the most desirable Carbon products are particulate organic carbon (POC), phytoplankton carbon (C_p) and dissolved organic Carbon (DOC). Algorithms for each of those products have been tested using a large collection of in-situ data assembled specifically for this purpose.

In addition to the algorithm testing, various experiments with dynamic models have been undertaken during this period, such as data assimilation and  evaluation dissolved carbon in global models.

This contribution summarises the results from the first phase of POCO and introduces the workplan for the remainder of the project.

The studying the mass blooms of marine phytoplankton requires new technologies; the urgency of this problem is determined, primarily, by practical importance:  the blooms may have significant impact on the environment (toxic blooms, eutrophication of coastal areas) and climatic conditions. At present the fast changes of the blooms parameters take place under the influence of anthropogenic impact and climate change, but the traditional methods do not provide the ability for adequate studying these changes. To solve the problem, we use an integrated approach, a combination of satellite observations, shipboard measurements and modeling. Ship measurements in several expeditions provided us with data needed for the development and verification of regional satellite algorithms, evaluation of dominant species of the bloom, cell concentration and biomass. The recent  ship measurements were based on advanced optical techniques, in particular  fluorescent methods.

In this presentation we focuse on the two most important blooms observed in European Seas of Russia: coccolithophore blooms in the Black and Barents Seas and cyanobacterial bloom in the Baltic Sea. The coccolithophore blooms have a significant impact on important physical and biogeochemical processes,  in particular the exchange of CO2  between the ocean and the atmosphere, and  global climate change; they are a part of  “biological pump”.  At present  the regional algorithms have been developed and validated by field measurements in the both seas. In the Black Sea the algorithm allows to separate changes associated with coccolithophore bloom and the river runoff effect. The maps of the monthly distributions of the coccolithophore cell concentration  in the Black Sea in June and in the Barents Sea in July-September were built  and diagrams of its inter-annual changes were constructed by using SeaWiFS and MODIS-Aqua ocean color data (http://optics.ocean.ru). The quantitative characteristics of blooms, in particular their area, intensity, temporal changes in different scales (from the bloom cycle time to inter-annual changes) were obtained from satellite data, and the relationship between the bloom characteristics and climatic factors (SST, PAR, wind speed and others)  was studied by using the  satellite data. In the Black Sea the close connection between changes in SST in February and intensity of  coccolithophore blooms in June was found: more intensive blooms were observed after cold winter, less intensive (or do not exist) after warm winter. The effect of coccolithophore blooms on the albedo of the water body was estimated.

In the Baltic Sea the field  studies of cyanobacterial bloom (with  contemporaneous satellite observations) were carried out in the Gulf of Finland in summer of 2012-2014 and in the southern part of the Baltic Sea during the transit scientific cruises from Kaliningrad to Arkhangelsk in 2014-2015.  In the former  the measured data included spectral values of the remote sensing reflectance,  Chl and TSM concentrations. The new regional algorithms have been developed, and direct comparison of the Chl and TSM values, derived from MODIS-Aqua data by the developed algorithms, with their values measured in situ showed a reasonable agreement. The spectra of the subsurface remote sensing reflectance, measured by a floating spectroradiometer, displayed the maxima near 575 and 650 nm caused by phycoerythrin and phycocyanin pigments specific to blue-green algae (cyanobacteria). We hope to use a new Ocean and Land Color Imager (OLCI) onboard of future Sentinel-3 satellite which has five spectral bands in the interval 500-700 nm, providing more opportunities for studying manifestation of  blue-green algae. 

This study was carried out on the account of a grant of No.14-17-00800 of the Russian Research Foundation at the P.P. Shirshov Institute of Oceanology, Russian Academy of Sciences. 

As the Earth’s climate system continues to change, tropical oceans are expected to become warmer, saltier, and less fertile. As one of the warmest and most saline ecosystems known, the Red Sea may provide valuable insights on the future functioning of tropical oceans. In contrast with general predictions for tropical seas, we observed that the Red Sea phytoplankton have increased abruptly during recent warmer climate periods. Our results suggest that changes in Earth climate processes (Indian monsoons and ENSO teleconnections) have led to a warmer but more fertile Red Sea. We have evidence that during warmer periods (ENSO+ phases), the influx of nutrient-rich waters into the Red Sea (from the Indian Ocean) increases, leading to a 75% increase in winter phytoplankton biomass.

In addition, although the Red Sea is known as a winter blooming environment, we have recently observed that most of the reef-bound coastal waters display equal or higher chlorophyll concentrations during summer (onset of summer monsoon). These summer blooms have not been studied adequately, as single-sensor missions suffer from severe data gaps during summer, primarily due to haze/clouds from intense heat. Here, we make use of the enhanced data coverage of the ESA Ocean Colour-CCI products in the Red Sea, to reveal different mechanisms that enhance phytoplankton biomass near the reefs. As revealed by the remotely-sensed ocean-colour observations, mesoscale eddies transfer fertile water masses to distant coral reef complexes between the Arabian peninsula and African side of the Red Sea.