Action #7: Forecast models for cyanobacterial blooms

Objectives: Forecast modelling of cyanobacteria growth and their blooms are challenging tasks but there is a growing demand for such action. Depending on the time horizon, different modelling tools may be used. Long-term scenario models (months) are based on ecosystem models supported by observations of nutrients as a key driver of blooms and expert opinions. The short-term models (days-weeks) may include more detailed parameterization of cyanobacteria vs. other algae groups and taking into account the weather as key drivers. This Action will analyse the performance of the forecast models for cyanobacterial blooms in the Gulf of Finland by comparing them to in-situ data obtained from the GoF PSS. Estimate the performance and greatest challenges of the current models and develop ideas on how the models could be advanced.

Action Lead and other Partners (with contact persons): FMI (Laakso), SYKE (Seppälä, Lehtinen)

JS3 Platforms included: FerryBox: Silja Serenade and Finnmaid (SYKE, FMI), Utö Observatory (FMI, SYKE), profiling buoys (FMI, SYKE), monitoring by R/V (SYKE, FMI).

Other data sources and external partners for implementation: ERGOM operational model forecasts for the Baltic Sea are available from The Baltic Monitoring Forecasting Centre (BAL MFC). Action has links #2 and #5, which target biogeochemical parameters.

Overall timetable of action: Data will be collected in 2021-22, model performance analysis and advancing modelling 2021-22.

Description of action: Action will improve the availability of observations for the modelling community. The adjustment of observations (time/location/parameters) will be jointly discussed. Though not each PSS partner is participating in this topic, the availability of additional data will be screened.

The Action will merge the relevant observation data from data producers for 2021 and 2022. Model performances will be analysed by comparing the results with in-situ data. This will follow by joint analyses to identify the biggest challenges in model performance. The outlook will be created, with the institutes involved, on how to further develop the models.

Best practices used or developed: The best practices as developed in GoF PSS #1 will be followed

Data flows: Data from Action #1 and # 4.

Data QC routines Quality controlled data from topics GoF PSS i) and GoF PSS iv) will be used.

Data management issues: No particular issues foreseen.

Expected results: Improved understanding of cyanobacterial bloom forecast model performance in the GoF area. Plans on improvements on models.

Users of results: Researchers, model developers, people responsible for giving cyanobacterial bloom information to the general public

Dissemination of results: Results will be shared by JERICO e-infrastructure.

Links: The topic will link WP2 T2.4 by analysing the current status of cyanobacteria modelling and providing a future outlook on how it needs to be advanced further.