Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100% of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO3, NH4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm

The exposure of the Great Barrier Reef to ocean acidification

The Great Barrier Reef (GBR) is founded on reef-building corals. Corals build their exoskeleton with aragonite, but ocean acidification is lowering the aragonite saturation state of seawater (Omega(a)). The downscaling of ocean acidification projections from global to GBR scales requires the set of regional drivers controlling Omega(a) to be resolved. Here we use a regional coupled circulation-biogeochemical model and observations to estimate the Omega(a) experienced by the 3,581 reefs of the GBR, and to apportion the contributions of the hydrological cycle, regional hydrodynamics and metabolism on Omega(a) variability. We find more detail, and a greater range (1.43), than previously compiled coarse maps of Omega(a) of the region (0.4), or in observations (1.0). Most of the variability in Omega(a) is due to processes upstream of the reef in question. As a result, future decline in Omega(a) is likely to be steeper on the GBR than currently projected by the IPCC assessment report

eReefs: An operational information system for managing the Great Barrier Reef

eReefs is a comprehensive interoperable information platform that has been developed for the Great Barrier Reef (GBR) region to provide users with access to improved environmental intelligence allowing them to assess past, present, and future conditions, as well as management options to mitigate the risks associated with multiple and sometimes competing uses of the GBR. eReefs is built upon an integrated system of data, catchment and marine models, visualisation, reporting and decision support tools that span the entire GBR area. This communication briefly describes eReefs architecture and components and provides examples of applications that have been used to inform policy and management decisions, and finally discusses challenges and key learnings and considers future developments and applications