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Tiles spelling the word 'research' on a pile of newspapers


Implementing Operational Ocean Monitoring and Forecasting Systems Chapter 9: Biogeochemical Modelling, page 249.
The guide aims to promote the development of new marine forecasting systems around the globe; along with the improvement of the existing ones. Indeed, it provides an overview of the value chain of an operational ocean forecasting system (OOFS) as well as international standards and best practices for setting up such a service.

A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts. 2022. Front. Mar. Sci., Sec. Ocean Observation. Ford D, Grossberg S, Rinaldi G, Menon P, Palmer M, Skakala J,  Smyth T, Wiliams C, Lorenzon Lopez A and Ciavatta S.

Abstract: This study presents a proof-of-concept for a fully automated and adaptive observing system for coastal ocean ecosystems. Such systems present a viable future observational framework for oceanography, reducing the cost and carbon footprint of marine research. An autonomous ocean robot (an ocean glider) was deployed for 11 weeks in the western English Channel and navigated by exchanging information with operational forecasting models. It aimed to track the onset and development of the spring phytoplankton bloom in 2021. A stochastic prediction model combined the real-time glider data with forecasts from an operational numerical model, which in turn assimilated the glider observations and other environmental data, to create high-resolution probabilistic predictions of phytoplankton and its chlorophyll signature. A series of waypoints were calculated at regular time intervals, to navigate the glider to where the phytoplankton bloom was most likely to be found. The glider successfully tracked the spring bloom at unprecedented temporal resolution, and the adaptive sampling strategy was shown to be feasible in an operational context. Assimilating the real-time glider data clearly improved operational biogeochemical forecasts when validated against independent observations at a nearby time series station, with a smaller impact at a more distant neighboring station. Remaining issues to be addressed were identified, for instance relating to quality control of near-real time data, accounting for differences between remote sensing and in situ observations, and extension to larger geographic domains. Based on these, recommendations are made for the development of future smart observing systems.

Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea. 2022. Remote Sens. 14, 1297. Shu C, Xiu P, Xing X, Qiu G, Ma W, Brewin R and Ciavatta S.
Abstract: Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements. 

The impact of ocean biogeochemistry on physics and its consequences for modelling shelf seas.  Ocean Modelling Volume 172, April 2022, 101976. Skalala J et al. 

Abstract: We use modelling and assimilation tools to explore the impact of biogeochemistry on physics in the shelf sea environment, using North-West European Shelf (NWES) as a case study. We demonstrate that such impact is significant: the attenuation of light by biogeochemical substances heats up the upper 20 m of the ocean by up to 1 °C and by a similar margin cools down the ocean within the 20–200 m range of depths. We demonstrate that these changes to sea temperature influence mixing in the upper ocean and feed back into marine biology by influencing the timing of the phytoplankton bloom, as suggested by the critical turbulence hypothesis. We compare different light schemes representing the impact of biogeochemistry on physics, and show that the physics is sensitive to both the spectral resolution of radiances and the represented optically active constituents. We introduce a new development into the research version of the operational model for the NWES, in which we calculate the heat fluxes based on the spectrally resolved attenuation by the simulated biogeochemical tracers, establishing a two-way coupling between biogeochemistry and physics. We demonstrate that in the late spring–summer the two-way coupled model increases heating in the upper oceanic layer compared to the existing model and improves by 1–3 days the timing of the simulated phytoplankton bloom. This improvement is relatively small compared with the existing model bias in bloom timing, but is sufficient to have a visible impact on model skill in the free run. We also validate the skill of the two-way coupling in the context of the weakly coupled physical–biogeochemical assimilation currently used for operational forecasting of the NWES. We show that the change to the skill is negligible for analyses, but it remains to be seen how much it differs for the forecasts.

Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis.  July 2021.  Reviews of Geophysics. Baatz R et al 
 Abstract: A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic-abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.

Towards a Multi-Platform Assimilative System for North Sea Biogeochemistry. 2021. JGR Oceans. Skakala J et al. 
Abstract: Oceanography has entered an era of new observing platforms, such as biogeochemical-Argo floats and gliders, some of which will provide three-dimensional maps of essential ecosystem variables on the North-West European (NWE) Shelf. In a foreseeable future operational centers will use multi-platform assimilation to integrate those valuable data into ecosystem reanalysis and forecast systems. Here we address some important questions related to glider biogeochemical data assimilation (DA) and introduce multi-platform DA in a preoperational model of the NWE Shelf sea ecosystem. We test the impact of the different multi-platform system components (glider vs. satellite, physical vs. biogeochemical) on the simulated biogeochemical variables. To characterize the model performance, we focus on the period around the phytoplankton spring bloom, since the bloom is a major ecosystem driver on the NWE Shelf. We found that the timing and magnitude of the phytoplankton bloom is insensitive to the physical DA, which is explained in the study. To correct the simulated phytoplankton bloom one needs to assimilate chlorophyll observations from glider or satellite Ocean Color (OC) into the model. Although outperformed by the glider chlorophyll assimilation, we show that OC assimilation has mostly desirable impact on the sub-surface chlorophyll. Since the OC assimilation updates chlorophyll only in the mixed layer, the impact on the sub-surface chlorophyll is the result of the model dynamical response to the assimilation. We demonstrate that the multi-platform assimilation combines the advantages of its components and always performs comparably to its best performing component.